Tag Archives: Healthcare

Building Simpler Genomics Workflows on AWS Step Functions

2018-11-29 Christie Gifrin

Post Syndicated from Christie Gifrin original https://aws.amazon.com/blogs/compute/building-simpler-genomics-workflows-on-aws-step-functions/

This post is courtesy of Ryan Ulaszek, AWS Genomics Partner Solutions Architect and Aaron Friedman, AWS Healthcare and Life Sciences Partner Solutions Architect

In 2017, we published a four part blog series on how to build a genomics workflow on AWS. In part 1, we introduced a general architecture highlighting three common layers: job, batch and workflow. In part 2, we described building the job layer with Docker and Amazon Elastic Container Registry (Amazon ECR). In part 3, we tackled the batch layer and built a batch engine using AWS Batch. In part 4, we built out the workflow layer using AWS Step Functions and AWS Lambda.

Since then, we’ve worked with many AWS customers and APN partners to implement this solution in genomics as well as in other workloads-of-interest. Today, we wanted to highlight a new feature in Step Functions that simplifies how customers and partners can build high-throughput genomics workflows on AWS.

Step Functions now supports native integration with AWS Batch, which simplifies how you can create an AWS Batch state that submits an asynchronous job and waits for that job to finish.

Before, you needed to build a state machine building block that submitted a job to AWS Batch, and then polled and checked its execution. Now, you can just submit the job to AWS Batch using the new AWS Batch task type. Step Functions waits to proceed until the job is completed. This reduces the complexity of your state machine and makes it easier to build a genomics workflow with asynchronous AWS Batch steps.

The new integrations include support for the following API actions:

AWS Batch SubmitJob
Amazon SNS Publish
Amazon SQS SendMessage
Amazon ECS RunTask
AWS Fargate RunTask
Amazon DynamoDB
- PutItem
- GetItem
- UpdateItem
- DeleteItem
Amazon SageMaker
- CreateTrainingJob
- CreateTransformJob
AWS Glue
- StartJobRun

You can also pass parameters to the service API. To use the new integrations, the role that you assume when running a state machine needs to have the appropriate permissions. For more information, see the AWS Step Functions Developer Guide.

Using a job status poller

In our 2017 post series, we created a job poller “pattern” with two separate Lambda functions. When the job finishes, the state machine proceeds to the next step and operates according to the necessary business logic. This is a useful pattern to manage asynchronous jobs when a direct integration is unavailable.

The steps in this building block state machine are as follows:

A job is submitted through a Lambda function.
The state machine queries the AWS Batch API for the job status in another Lambda function.
The job status is checked to see if the job has completed. If the job status equals SUCCESS, the final job status is logged. If the job status equals FAILED, the execution of the state machine ends. In all other cases, wait 30 seconds and go back to Step 2.

Both of the Submit Job and Get Job Lambda functions are available as example Lambda functions in the console. The job status poller is available in the Step Functions console as a sample project.

Here is the JSON representing this state machine.

{
  "Comment": "A simple example that submits a job to AWS Batch",
  "StartAt": "SubmitJob",
  "States": {
    "SubmitJob": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:us-east-1:<account-id>::function:batchSubmitJob",
      "Next": "GetJobStatus"
    },
    "GetJobStatus": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:us-east-1:<account-id>:function:batchGetJobStatus",
      "Next": "CheckJobStatus",
      "InputPath": "$",
      "ResultPath": "$.status"
    },
    "CheckJobStatus": {
      "Type": "Choice",
      "Choices": [
        {
          "Variable": "$.status",
          "StringEquals": "FAILED",
          "End": true
        },
        {
          "Variable": "$.status",
          "StringEquals": "SUCCEEDED",
          "Next": "GetFinalJobStatus"
        }
      ],
      "Default": "Wait30Seconds"
    },
    "Wait30Seconds": {
      "Type": "Wait",
      "Seconds": 30,
      "Next": "GetJobStatus"
    },
    "GetFinalJobStatus": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:us-east-1:<account-id>:function:batchGetJobStatus",
      "End": true
    }
  }
}

With Step Functions Service Integrations

With Step Functions service integrations, it is now simpler to submit and wait for an AWS Batch job, or any other supported service.

The following code block is the JSON representing the new state machine for an asynchronous batch job. If you are familiar with the AWS Batch SubmitJob API action, you may notice that the parameters are consistent with what you would see in that API call. You can also use the optional AWS Batch parameters in addition to JobDefinition, JobName, and JobQueue.

{
 "StartAt": "RunBatchJob",
 "States": {
     "RunIsaacJob":{
     "Type":"Task",
     "Resource":"arn:aws:states:::batch:submitJob.sync",
     "Parameters":{
        "JobDefinition":"Isaac",
        "JobName.$":"$.isaac.JobName",
        "JobQueue":"HighPriority",
        "Parameters.$": "$.isaac"
     },
     "TimeoutSeconds": 900,
     "HeartbeatSeconds": 60,
     "Next":"Parallel",
     "InputPath":"$",
     "ResultPath":"$.status",
     "Retry" : [
        {
          "ErrorEquals": [ "States.Timeout" ],
          "IntervalSeconds": 3,
          "MaxAttempts": 2,
          "BackoffRate": 1.5
        }
     ]
  }
}

Here is an example of the workflow input JSON. Pass all of the container parameters that were being constructed in the submit job Lambda function.

{
  "isaac": {
    "WorkingDir": "/scratch",
    "JobName": "isaac-1",
    "FastQ1S3Path": "s3://aws-batch-genomics-resources/fastq/SRR1919605_1.fastq.gz",
    "BAMS3FolderPath": "s3://aws-batch-genomics-resources/fastq/SRR1919605_2.fastq.gz",
    "FastQ2S3Path": "s3://bccn-genome-data/fastq/NIST7035_R2_trimmed.fastq.gz",
    "ReferenceS3Path": "s3://aws-batch-genomics-resources/reference/isaac/"
  }
}

When you deploy the job definition, add the command attribute that was previously being constructed in the Lambda function launching the AWS Batch job.

IsaacJobDefinition:
    Type: AWS::Batch::JobDefinition
    Properties:
      JobDefinitionName: "Isaac"
      Type: container
      RetryStrategy:
        Attempts: 1
      Parameters:
        BAMS3FolderPath: !Sub "s3://${JobResultsBucket}/NA12878_states_1/bam"
        FastQ1S3Path: "s3://aws-batch-genomics-resources/fastq/SRR1919605_1.fastq.gz"
        FastQ2S3Path: "s3://aws-batch-genomics-resources/fastq/SRR1919605_2.fastq.gz"
        ReferenceS3Path: "s3://aws-batch-genomics-resources/reference/isaac/"
        WorkingDir: "/scratch"
      ContainerProperties:
        Image: "rulaszek/isaac"
        Vcpus: 32
        Memory: 80000
        JobRoleArn:
          Fn::ImportValue: !Sub "${RoleStackName}:ECSTaskRole"
        Command:
          - "--bam_s3_folder_path"
          - "Ref::BAMS3FolderPath"
          - "--fastq1_s3_path"
          - "Ref::FastQ1S3Path"
          - "--fastq2_s3_path"
          - "Ref::FastQ2S3Path"
          - "--reference_s3_path"
          - "Ref::ReferenceS3Path"
          - "--working_dir"
          - "Ref::WorkingDir"
        MountPoints:
          - ContainerPath: "/scratch"
            ReadOnly: false
            SourceVolume: docker_scratch
        Volumes:
          - Name: docker_scratch
            Host:
              SourcePath: "/docker_scratch"

The key-value parameters passed into the workflow are mapped using Parameters.$ to the values in the job definition using the keys. Value substitutions do take place. The Docker run looks like the following:

docker run <isaac_container_uri> --bam_s3_folder_path s3://batch-genomics-pipeline-jobresultsbucket-1kzdu216m2b0k/NA12878_states_3/bam
                                 --fastq1_s3_path s3://aws-batch-genomics-resources/fastq/SRR1919605_1.fastq.gz
                                 --fastq2_s3_path s3://aws-batch-genomics-resources/fastq/SRR1919605_2.fastq.gz 
                                 --reference_s3_path s3://aws-batch-genomics-resources/reference/isaac/ 
                                 --working_dir /scratch

Genomics workflow: Before and after

Overall, connectors dramatically simplify your genomics workflow. The following workflow is a simple genomics secondary analysis pipeline, which we highlighted in our original post series.

The first step aligns the sample against a reference genome. When alignment is complete, variant calling and QA metrics are calculated in two parallel steps. When variant calling is complete, variant annotation is performed. Before, our genomics workflow looked like this:

Now it looks like this:

Here is the new workflow JSON:

{
   "Comment":"A simple genomics secondary-analysis workflow",
   "StartAt":"RunIsaacJob",
   "States":{
      "RunIsaacJob":{
         "Type":"Task",
         "Resource":"arn:aws:states:::batch:submitJob.sync",
         "Parameters":{
            "JobDefinition":"Isaac",
            "JobName.$":"$.isaac.JobName",
            "JobQueue":"HighPriority",
            "Parameters.$": "$.isaac"
         },
         "TimeoutSeconds": 900,
         "HeartbeatSeconds": 60,
         "Next":"Parallel",
         "InputPath":"$",
         "ResultPath":"$.status",
         "Retry" : [
            {
              "ErrorEquals": [ "States.Timeout" ],
              "IntervalSeconds": 3,
              "MaxAttempts": 2,
              "BackoffRate": 1.5
            }
         ]
      },
      "Parallel":{
         "Type":"Parallel",
         "Next":"FinalState",
         "Branches":[
            {
               "StartAt":"RunStrelkaJob",
               "States":{
                  "RunStrelkaJob":{
                     "Type":"Task",
                     "Resource":"arn:aws:states:::batch:submitJob.sync",
                     "Parameters":{
                        "JobDefinition":"Strelka",
                        "JobName.$":"$.strelka.JobName",
                        "JobQueue":"HighPriority",
                        "Parameters.$": "$.strelka"
                     },
                     "TimeoutSeconds": 900,
                     "HeartbeatSeconds": 60,
                     "Next":"RunSnpEffJob",
                     "InputPath":"$",
                     "ResultPath":"$.status",
                     "Retry" : [
                        {
                          "ErrorEquals": [ "States.Timeout" ],
                          "IntervalSeconds": 3,
                          "MaxAttempts": 2,
                          "BackoffRate": 1.5
                        }
                     ]
                  },
                  "RunSnpEffJob":{
                     "Type":"Task",
                     "Resource":"arn:aws:states:::batch:submitJob.sync",
                     "Parameters":{
                        "JobDefinition":"SNPEff",
                        "JobName.$":"$.snpeff.JobName",
                        "JobQueue":"HighPriority",
                        "Parameters.$": "$.snpeff"
                     },
                     "TimeoutSeconds": 900,
                     "HeartbeatSeconds": 60,
                     "Retry" : [
                        {
                          "ErrorEquals": [ "States.Timeout" ],
                          "IntervalSeconds": 3,
                          "MaxAttempts": 2,
                          "BackoffRate": 1.5
                        }
                     ],
                     "End":true
                  }
               }
            },
            {
               "StartAt":"RunSamtoolsStatsJob",
               "States":{
                  "RunSamtoolsStatsJob":{
                     "Type":"Task",
                     "Resource":"arn:aws:states:::batch:submitJob.sync",
                     "Parameters":{
                        "JobDefinition":"SamtoolsStats",
                        "JobName.$":"$.samtools.JobName",
                        "JobQueue":"HighPriority",
                        "Parameters.$": "$.samtools"
                     },
                     "TimeoutSeconds": 900,
                     "HeartbeatSeconds": 60,
                     "End":true,
                     "Retry" : [
                        {
                          "ErrorEquals": [ "States.Timeout" ],
                          "IntervalSeconds": 3,
                          "MaxAttempts": 2,
                          "BackoffRate": 1.5
                        }
                     ]
                  }
               }
            }
         ]
      },
      "FinalState":{
         "Type":"Pass",
         "End":true
      }
   }
}

Here is the new Amazon CloudFormation template for deploying the AWS Batch job definitions for each tool:

AWSTemplateFormatVersion: 2010-09-09

Description: Batch job definitions for batch genomics

Parameters:
  RoleStackName:
    Description: "Stack that deploys roles for genomic workflow"
    Type: String
  VPCStackName:
    Description: "Stack that deploys vps for genomic workflow"
    Type: String
  JobResultsBucket:
    Description: "Bucket that holds workflow job results"
    Type: String

Resources:
  IsaacJobDefinition:
    Type: AWS::Batch::JobDefinition
    Properties:
      JobDefinitionName: "Isaac"
      Type: container
      RetryStrategy:
        Attempts: 1
      Parameters:
        BAMS3FolderPath: !Sub "s3://${JobResultsBucket}/NA12878_states_1/bam"
        FastQ1S3Path: "s3://aws-batch-genomics-resources/fastq/SRR1919605_1.fastq.gz"
        FastQ2S3Path: "s3://aws-batch-genomics-resources/fastq/SRR1919605_2.fastq.gz"
        ReferenceS3Path: "s3://aws-batch-genomics-resources/reference/isaac/"
        WorkingDir: "/scratch"
      ContainerProperties:
        Image: "rulaszek/isaac"
        Vcpus: 32
        Memory: 80000
        JobRoleArn:
          Fn::ImportValue: !Sub "${RoleStackName}:ECSTaskRole"
        Command:
          - "--bam_s3_folder_path"
          - "Ref::BAMS3FolderPath"
          - "--fastq1_s3_path"
          - "Ref::FastQ1S3Path"
          - "--fastq2_s3_path"
          - "Ref::FastQ2S3Path"
          - "--reference_s3_path"
          - "Ref::ReferenceS3Path"
          - "--working_dir"
          - "Ref::WorkingDir"
        MountPoints:
          - ContainerPath: "/scratch"
            ReadOnly: false
            SourceVolume: docker_scratch
        Volumes:
          - Name: docker_scratch
            Host:
              SourcePath: "/docker_scratch"

  StrelkaJobDefinition:
    Type: AWS::Batch::JobDefinition
    Properties:
      JobDefinitionName: "Strelka"
      Type: container
      RetryStrategy:
        Attempts: 1
      Parameters:
        BAMS3Path: !Sub "s3://${JobResultsBucket}/NA12878_states_1/bam/sorted.bam"
        BAIS3Path: !Sub "s3://${JobResultsBucket}/NA12878_states_1/bam/sorted.bam.bai"
        ReferenceS3Path: "s3://aws-batch-genomics-resources/reference/hg38.fa"
        ReferenceIndexS3Path: "s3://aws-batch-genomics-resources/reference/hg38.fa.fai"
        VCFS3Path: !Sub "s3://${JobResultsBucket}/NA12878_states_1/vcf"
        WorkingDir: "/scratch"
      ContainerProperties:
        Image: "rulaszek/strelka"
        Vcpus: 32
        Memory: 32000
        JobRoleArn:
          Fn::ImportValue: !Sub "${RoleStackName}:ECSTaskRole"
        Command:
          - "--bam_s3_path"
          - "Ref::BAMS3Path"
          - "--bai_s3_path"
          - "Ref::BAIS3Path"
          - "--reference_s3_path"
          - "Ref::ReferenceS3Path"
          - "--reference_index_s3_path"
          - "Ref::ReferenceIndexS3Path"
          - "--vcf_s3_path"
          - "Ref::VCFS3Path"
          - "--working_dir"
          - "Ref::WorkingDir"
        MountPoints:
          - ContainerPath: "/scratch"
            ReadOnly: false
            SourceVolume: docker_scratch
        Volumes:
          - Name: docker_scratch
            Host:
              SourcePath: "/docker_scratch"

  SnpEffJobDefinition:
    Type: AWS::Batch::JobDefinition
    Properties:
      JobDefinitionName: "SNPEff"
      Type: container
      RetryStrategy:
        Attempts: 1
      Parameters:
        VCFS3Path: !Sub "s3://${JobResultsBucket}/NA12878_states_1/vcf/variants/genome.vcf.gz"
        AnnotatedVCFS3Path: !Sub "s3://${JobResultsBucket}/NA12878_states_1/vcf/genome.anno.vcf"
        CommandArgs: " -t hg38 "
        WorkingDir: "/scratch"
      ContainerProperties:
        Image: "rulaszek/snpeff"
        Vcpus: 4
        Memory: 10000
        JobRoleArn:
          Fn::ImportValue: !Sub "${RoleStackName}:ECSTaskRole"
        Command:
          - "--annotated_vcf_s3_path"
          - "Ref::AnnotatedVCFS3Path"
          - "--vcf_s3_path"
          - "Ref::VCFS3Path"
          - "--cmd_args"
          - "Ref::CommandArgs"
          - "--working_dir"
          - "Ref::WorkingDir"
        MountPoints:
          - ContainerPath: "/scratch"
            ReadOnly: false
            SourceVolume: docker_scratch
        Volumes:
          - Name: docker_scratch
            Host:
              SourcePath: "/docker_scratch"

  SamtoolsStatsJobDefinition:
    Type: AWS::Batch::JobDefinition
    Properties:
      JobDefinitionName: "SamtoolsStats"
      Type: container
      RetryStrategy:
        Attempts: 1
      Parameters:
        ReferenceS3Path: "s3://aws-batch-genomics-resources/reference/hg38.fa"
        BAMS3Path: !Sub "s3://${JobResultsBucket}/NA12878_states_1/bam/sorted.bam"
        BAMStatsS3Path: !Sub "s3://${JobResultsBucket}/NA12878_states_1/bam/sorted.bam.stats"
        WorkingDir: "/scratch"
      ContainerProperties:
        Image: "rulaszek/samtools-stats"
        Vcpus: 4
        Memory: 10000
        JobRoleArn:
          Fn::ImportValue: !Sub "${RoleStackName}:ECSTaskRole"
        Command:
          - "--bam_s3_path"
          - "Ref::BAMS3Path"
          - "--bam_stats_s3_path"
          - "Ref::BAMStatsS3Path"
          - "--reference_s3_path"
          - "Ref::ReferenceS3Path"
          - "--working_dir"
          - "Ref::WorkingDir"
        MountPoints:
          - ContainerPath: "/scratch"
            ReadOnly: false
            SourceVolume: docker_scratch
        Volumes:
          - Name: docker_scratch
            Host:
              SourcePath: "/docker_scratch"

Here is the new CloudFormation script that deploys the new workflow:

AWSTemplateFormatVersion: 2010-09-09

Description: State Machine for batch benomics

Parameters:
  RoleStackName:
    Description: "Stack that deploys roles for genomic workflow"
    Type: String
  VPCStackName:
    Description: "Stack that deploys vps for genomic workflow"
    Type: String

Resources:
  # S3
  GenomicWorkflow:
    Type: AWS::StepFunctions::StateMachine
    Properties:
      RoleArn:
        Fn::ImportValue: !Sub "${RoleStackName}:StatesExecutionRole"
      DefinitionString: !Sub |-
        {
           "Comment":"A simple example that submits a job to AWS Batch",
           "StartAt":"RunIsaacJob",
           "States":{
              "RunIsaacJob":{
                 "Type":"Task",
                 "Resource":"arn:aws:states:::batch:submitJob.sync",
                 "Parameters":{
                    "JobDefinition":"Isaac",
                    "JobName.$":"$.isaac.JobName",
                    "JobQueue":"HighPriority",
                    "Parameters.$": "$.isaac"
                 },
                 "TimeoutSeconds": 900,
                 "HeartbeatSeconds": 60,
                 "Next":"Parallel",
                 "InputPath":"$",
                 "ResultPath":"$.status",
                 "Retry" : [
                    {
                      "ErrorEquals": [ "States.Timeout" ],
                      "IntervalSeconds": 3,
                      "MaxAttempts": 2,
                      "BackoffRate": 1.5
                    }
                 ]
              },
              "Parallel":{
                 "Type":"Parallel",
                 "Next":"FinalState",
                 "Branches":[
                    {
                       "StartAt":"RunStrelkaJob",
                       "States":{
                          "RunStrelkaJob":{
                             "Type":"Task",
                             "Resource":"arn:aws:states:::batch:submitJob.sync",
                             "Parameters":{
                                "JobDefinition":"Strelka",
                                "JobName.$":"$.strelka.JobName",
                                "JobQueue":"HighPriority",
                                "Parameters.$": "$.strelka"
                             },
                             "TimeoutSeconds": 900,
                             "HeartbeatSeconds": 60,
                             "Next":"RunSnpEffJob",
                             "InputPath":"$",
                             "ResultPath":"$.status",
                             "Retry" : [
                                {
                                  "ErrorEquals": [ "States.Timeout" ],
                                  "IntervalSeconds": 3,
                                  "MaxAttempts": 2,
                                  "BackoffRate": 1.5
                                }
                             ]
                          },
                          "RunSnpEffJob":{
                             "Type":"Task",
                             "Resource":"arn:aws:states:::batch:submitJob.sync",
                             "Parameters":{
                                "JobDefinition":"SNPEff",
                                "JobName.$":"$.snpeff.JobName",
                                "JobQueue":"HighPriority",
                                "Parameters.$": "$.snpeff"
                             },
                             "TimeoutSeconds": 900,
                             "HeartbeatSeconds": 60,
                             "Retry" : [
                                {
                                  "ErrorEquals": [ "States.Timeout" ],
                                  "IntervalSeconds": 3,
                                  "MaxAttempts": 2,
                                  "BackoffRate": 1.5
                                }
                             ],
                             "End":true
                          }
                       }
                    },
                    {
                       "StartAt":"RunSamtoolsStatsJob",
                       "States":{
                          "RunSamtoolsStatsJob":{
                             "Type":"Task",
                             "Resource":"arn:aws:states:::batch:submitJob.sync",
                             "Parameters":{
                                "JobDefinition":"SamtoolsStats",
                                "JobName.$":"$.samtools.JobName",
                                "JobQueue":"HighPriority",
                                "Parameters.$": "$.samtools"
                             },
                             "TimeoutSeconds": 900,
                             "HeartbeatSeconds": 60,
                             "End":true,
                             "Retry" : [
                                {
                                  "ErrorEquals": [ "States.Timeout" ],
                                  "IntervalSeconds": 3,
                                  "MaxAttempts": 2,
                                  "BackoffRate": 1.5
                                }
                             ]
                          }
                       }
                    }
                 ]
              },
              "FinalState":{
                 "Type":"Pass",
                 "End":true
              }
           }
        }

Outputs:
  GenomicsWorkflowArn:
    Description: GenomicWorkflow ARN
    Value: !Ref GenomicWorkflow
  StackName:
    Description: StackName
    Value: !Sub ${AWS::StackName}

Conclusion

AWS Step Functions service integrations are a great way to simplify creating complex workflows with asynchronous steps. While we highlighted the use case with AWS Batch today, there are many other ways that healthcare and life sciences customers can use this new feature, such as with message processing.

For more information about how AWS can enable your genomics workloads, be sure to check out the AWS Genomics page.

We’ve updated the open-source project to take advantage of the new AWS Batch integration in Step Functions. You can find the changes aws-batch-genomics/tree/v2.0.0 folder.

Original posts in this four-part series:

Happy coding!

10 visualizations to try in Amazon QuickSight with sample data

2018-04-27 Karthik Kumar Odapally

Post Syndicated from Karthik Kumar Odapally original https://aws.amazon.com/blogs/big-data/10-visualizations-to-try-in-amazon-quicksight-with-sample-data/

If you’re not already familiar with building visualizations for quick access to business insights using Amazon QuickSight, consider this your introduction. In this post, we’ll walk through some common scenarios with sample datasets to provide an overview of how you can connect yuor data, perform advanced analysis and access the results from any web browser or mobile device.

The following visualizations are built from the public datasets available in the links below. Before we jump into that, let’s take a look at the supported data sources, file formats and a typical QuickSight workflow to build any visualization.

Which data sources does Amazon QuickSight support?

At the time of publication, you can use the following data methods:

Connect to AWS data sources, including:
- Amazon RDS
- Amazon Aurora
- Amazon Redshift
- Amazon Athena
- Amazon S3
Upload Excel spreadsheets or flat files (CSV, TSV, CLF, and ELF)
Connect to on-premises databases like Teradata, SQL Server, MySQL, and PostgreSQL
Import data from SaaS applications like Salesforce and Snowflake
Use big data processing engines like Spark and Presto

This list is constantly growing. For more information, see Supported Data Sources.

Answers in instants

SPICE is the Amazon QuickSight super-fast, parallel, in-memory calculation engine, designed specifically for ad hoc data visualization. SPICE stores your data in a system architected for high availability, where it is saved until you choose to delete it. Improve the performance of database datasets by importing the data into SPICE instead of using a direct database query. To calculate how much SPICE capacity your dataset needs, see Managing SPICE Capacity.

Typical Amazon QuickSight workflow

When you create an analysis, the typical workflow is as follows:

Connect to a data source, and then create a new dataset or choose an existing dataset.
(Optional) If you created a new dataset, prepare the data (for example, by changing field names or data types).
Create a new analysis.
Add a visual to the analysis by choosing the fields to visualize. Choose a specific visual type, or use AutoGraph and let Amazon QuickSight choose the most appropriate visual type, based on the number and data types of the fields that you select.
(Optional) Modify the visual to meet your requirements (for example, by adding a filter or changing the visual type).
(Optional) Add more visuals to the analysis.
(Optional) Add scenes to the default story to provide a narrative about some aspect of the analysis data.
(Optional) Publish the analysis as a dashboard to share insights with other users.

The following graphic illustrates a typical Amazon QuickSight workflow.

Visualizations created in Amazon QuickSight with sample datasets

Visualizations for a data analyst

Source: https://data.worldbank.org/

Download and Resources: https://datacatalog.worldbank.org/dataset/world-development-indicators

Data catalog: The World Bank invests into multiple development projects at the national, regional, and global levels. It’s a great source of information for data analysts.

The following graph shows the percentage of the population that has access to electricity (rural and urban) during 2000 in Asia, Africa, the Middle East, and Latin America.

The following graph shows the share of healthcare costs that are paid out-of-pocket (private vs. public). Also, you can maneuver over the graph to get detailed statistics at a glance.

Visualizations for a trading analyst

Source: Deutsche Börse Public Dataset (DBG PDS)

Download and resources: https://aws.amazon.com/public-datasets/deutsche-boerse-pds/

Data catalog: The DBG PDS project makes real-time data derived from Deutsche Börse’s trading market systems available to the public for free. This is the first time that such detailed financial market data has been shared freely and continually from the source provider.

The following graph shows the market trend of max trade volume for different EU banks. It builds on the data available on XETRA engines, which is made up of a variety of equities, funds, and derivative securities. This graph can be scrolled to visualize trade for a period of an hour or more.

The following graph shows the common stock beating the rest of the maximum trade volume over a period of time, grouped by security type.

Visualizations for a data scientist

Source: https://catalog.data.gov/

Download and resources: https://catalog.data.gov/dataset/road-weather-information-stations-788f8

Data catalog: Data derived from different sensor stations placed on the city bridges and surface streets are a core information source. The road weather information station has a temperature sensor that measures the temperature of the street surface. It also has a sensor that measures the ambient air temperature at the station each second.

The following graph shows the present max air temperature in Seattle from different RWI station sensors.

The following graph shows the minimum temperature of the road surface at different times, which helps predicts road conditions at a particular time of the year.

Visualizations for a data engineer

Source: https://www.kaggle.com/

Download and resources: https://www.kaggle.com/datasnaek/youtube-new/data

Data catalog: Kaggle has come up with a platform where people can donate open datasets. Data engineers and other community members can have open access to these datasets and can contribute to the open data movement. They have more than 350 datasets in total, with more than 200 as featured datasets. It has a few interesting datasets on the platform that are not present at other places, and it’s a platform to connect with other data enthusiasts.

The following graph shows the trending YouTube videos and presents the max likes for the top 20 channels. This is one of the most popular datasets for data engineers.

The following graph shows the YouTube daily statistics for the max views of video titles published during a specific time period.

Visualizations for a business user

Source: New York Taxi Data

Download and resources: https://data.cityofnewyork.us/Transportation/2016-Green-Taxi-Trip-Data/hvrh-b6nb

Data catalog: NYC Open data hosts some very popular open data sets for all New Yorkers. This platform allows you to get involved in dive deep into the data set to pull some useful visualizations. 2016 Green taxi trip dataset includes trip records from all trips completed in green taxis in NYC in 2016. Records include fields capturing pick-up and drop-off dates/times, pick-up and drop-off locations, trip distances, itemized fares, rate types, payment types, and driver-reported passenger counts.

The following graph presents maximum fare amount grouped by the passenger count during a period of time during a day. This can be further expanded to follow through different day of the month based on the business need.

The following graph shows the NewYork taxi data from January 2016, showing the dip in the number of taxis ridden on January 23, 2016 across all types of taxis.

A quick search for that date and location shows you the following news report:

Summary

Using Amazon QuickSight, you can see patterns across a time-series data by building visualizations, performing ad hoc analysis, and quickly generating insights. We hope you’ll give it a try today!

Additional Reading

If you found this post useful, be sure to check out Amazon QuickSight Adds Support for Combo Charts and Row-Level Security and Visualize AWS Cloudtrail Logs Using AWS Glue and Amazon QuickSight.

Karthik Odapally is a Sr. Solutions Architect in AWS. His passion is to build cost effective and highly scalable solutions on the cloud. In his spare time, he bakes cookies and cupcakes for family and friends here in the PNW. He loves vintage racing cars.

Pranabesh Mandal is a Solutions Architect in AWS. He has over a decade of IT experience. He is passionate about cloud technology and focuses on Analytics. In his spare time, he likes to hike and explore the beautiful nature and wild life of most divine national parks around the United States alongside his wife.

Store, Protect, Optimize Your Healthcare Data with AWS

2018-04-26 Stephen Jepsen

Post Syndicated from Stephen Jepsen original https://aws.amazon.com/blogs/architecture/store-protect-optimize-your-healthcare-data-with-aws/

This blog post was co-authored by Ujjwal Ratan, a senior AI/ML solutions architect on the global life sciences team.

Healthcare data is generated at an ever-increasing rate and is predicted to reach 35 zettabytes by 2020. Being able to cost-effectively and securely manage this data whether for patient care, research or legal reasons is increasingly important for healthcare providers.

Healthcare providers must have the ability to ingest, store and protect large volumes of data including clinical, genomic, device, financial, supply chain, and claims. AWS is well-suited to this data deluge with a wide variety of ingestion, storage and security services (e.g. AWS Direct Connect, Amazon Kinesis Streams, Amazon S3, Amazon Macie) for customers to handle their healthcare data. In a recent Healthcare IT News article, healthcare thought-leader, John Halamka, noted, “I predict that five years from now none of us will have datacenters. We’re going to go out to the cloud to find EHRs, clinical decision support, analytics.”

I realize simply storing this data is challenging enough. Magnifying the problem is the fact that healthcare data is increasingly attractive to cyber attackers, making security a top priority. According to Mariya Yao in her Forbes column, it is estimated that individual medical records can be worth hundreds or even thousands of dollars on the black market.

In this first of a 2-part post, I will address the value that AWS can bring to customers for ingesting, storing and protecting provider’s healthcare data. I will describe key components of any cloud-based healthcare workload and the services AWS provides to meet these requirements. In part 2 of this post we will dive deep into the AWS services used for advanced analytics, artificial intelligence and machine learning.

The data tsunami is upon us

So where is this data coming from? In addition to the ubiquitous electronic health record (EHR), the sources of this data include:

genomic sequencers
devices such as MRIs, x-rays and ultrasounds
sensors and wearables for patients
medical equipment telemetry
mobile applications

Additional sources of data come from non-clinical, operational systems such as:

human resources
finance
supply chain
claims and billing

Data from these sources can be structured (e.g., claims data) as well as unstructured (e.g., clinician notes). Some data comes across in streams such as that taken from patient monitors, while some comes in batch form. Still other data comes in near-real time such as HL7 messages. All of this data has retention policies dictating how long it must be stored. Much of this data is stored in perpetuity as many systems in use today have no purge mechanism. AWS has services to manage all these data types as well as their retention, security and access policies.

Imaging is a significant contributor to this data tsunami. Increasing demand for early-stage diagnoses along with aging populations drive increasing demand for images from CT, PET, MRI, ultrasound, digital pathology, X-ray and fluoroscopy. For example, a thin-slice CT image can be hundreds of megabytes. Increasing demand and strict retention policies make storage costly.

Due to the plummeting cost of gene sequencing, molecular diagnostics (including liquid biopsy) is a large contributor to this data deluge. Many predict that as the value of molecular testing becomes more identifiable, the reimbursement models will change and it will increasingly become the standard of care. According to the Washington Post article “Sequencing the Genome Creates so Much Data We Don’t Know What to do with It,”

“Some researchers predict that up to one billion people will have their genome sequenced by 2025 generating up to 40 exabytes of data per year.”

Although genomics is primarily used for oncology diagnostics today, it’s also used for other purposes, pharmacogenomics — used to understand how an individual will metabolize a medication.

Reference Architecture

It is increasingly challenging for the typical hospital, clinic or physician practice to securely store, process and manage this data without cloud adoption.

Amazon has a variety of ingestion techniques depending on the nature of the data including size, frequency and structure. AWS Snowball and AWS Snowmachine are appropriate for extremely-large, secure data transfers whether one time or episodic. AWS Glue is a fully-managed ETL service for securely moving data from on-premise to AWS and Amazon Kinesis can be used for ingesting streaming data.

Amazon S3, Amazon S3 IA, and Amazon Glacier are economical, data-storage services with a pay-as-you-go pricing model that expand (or shrink) with the customer’s requirements.

The above architecture has four distinct components – ingestion, storage, security, and analytics. In this post I will dive deeper into the first three components, namely ingestion, storage and security. In part 2, I will look at how to use AWS’ analytics services to draw value on, and optimize, your healthcare data.

Ingestion

A typical provider data center will consist of many systems with varied datasets. AWS provides multiple tools and services to effectively and securely connect to these data sources and ingest data in various formats. The customers can choose from a range of services and use them in accordance with the use case.

For use cases involving one-time (or periodic), very large data migrations into AWS, customers can take advantage of AWS Snowball devices. These devices come in two sizes, 50 TB and 80 TB and can be combined together to create a petabyte scale data transfer solution.

The devices are easy to connect and load and they are shipped to AWS avoiding the network bottlenecks associated with such large-scale data migrations. The devices are extremely secure supporting 256-bit encryption and come in a tamper-resistant enclosure. AWS Snowball imports data in Amazon S3 which can then interface with other AWS compute services to process that data in a scalable manner.

For use cases involving a need to store a portion of datasets on premises for active use and offload the rest on AWS, the Amazon storage gateway service can be used. The service allows you to seamlessly integrate on premises applications via standard storage protocols like iSCSI or NFS mounted on a gateway appliance. It supports a file interface, a volume interface and a tape interface which can be utilized for a range of use cases like disaster recovery, backup and archiving, cloud bursting, storage tiering and migration.

The AWS Storage Gateway appliance can use the AWS Direct Connect service to establish a dedicated network connection from the on premises data center to AWS.

Specific Industry Use Cases

By using the AWS proposed reference architecture for disaster recovery, healthcare providers can ensure their data assets are securely stored on the cloud and are easily accessible in the event of a disaster. The “AWS Disaster Recovery” whitepaper includes details on options available to customers based on their desired recovery time objective (RTO) and recovery point objective (RPO).

AWS is an ideal destination for offloading large volumes of less-frequently-accessed data. These datasets are rarely used in active compute operations but are exceedingly important to retain for reasons like compliance. By storing these datasets on AWS, customers can take advantage of the highly-durable platform to securely store their data and also retrieve them easily when they need to. For more details on how AWS enables customers to run back and archival use cases on AWS, please refer to the following set of whitepapers.

A healthcare provider may have a variety of databases spread throughout the hospital system supporting critical applications such as EHR, PACS, finance and many more. These datasets often need to be aggregated to derive information and calculate metrics to optimize business processes. AWS Glue is a fully-managed Extract, Transform and Load (ETL) service that can read data from a JDBC-enabled, on-premise database and transfer the datasets into AWS services like Amazon S3, Amazon Redshift and Amazon RDS. This allows customers to create transformation workflows that integrate smaller datasets from multiple sources and aggregates them on AWS.

Healthcare providers deal with a variety of streaming datasets which often have to be analyzed in near real time. These datasets come from a variety of sources such as sensors, messaging buses and social media, and often do not adhere to an industry standard. The Amazon Kinesis suite of services, that includes Amazon Kinesis Streams, Amazon Kinesis Firehose, and Amazon Kinesis Analytics, are the ideal set of services to accomplish the task of deriving value from streaming data.

Example: Using AWS Glue to de-identify and ingest healthcare data into S3
Let’s consider a scenario in which a provider maintains patient records in a database they want to ingest into S3. The provider also wants to de-identify the data by stripping personally- identifiable attributes and store the non-identifiable information in an S3 bucket. This bucket is different from the one that contains identifiable information. Doing this allows the healthcare provider to separate sensitive information with more restrictions set up via S3 bucket policies.

To ingest records into S3, we create a Glue job that reads from the source database using a Glue connection. The connection is also used by a Glue crawler to populate the Glue data catalog with the schema of the source database. We will use the Glue development endpoint and a zeppelin notebook server on EC2 to develop and execute the job.

Step 1: Import the necessary libraries and also set a glue context which is a wrapper on the spark context:

Step 2: Create a dataframe from the source data. I call the dataframe “readmissionsdata”. Here is what the schema would look like:

Step 3: Now select the columns that contains indentifiable information and store it in a new dataframe. Call the new dataframe “phi”.

Step 4: Non-PHI columns are stored in a separate dataframe. Call this dataframe “nonphi”.

Step 5: Write the two dataframes into two separate S3 buckets

Once successfully executed, the PHI and non-PHI attributes are stored in two separate files in two separate buckets that can be individually maintained.

Storage

In 2016, 327 healthcare providers reported a protected health information (PHI) breach, affecting 16.4m patient records[1]. There have been 342 data breaches reported in 2017 — involving 3.2 million patient records.[2]

To date, AWS has released 51 HIPAA-eligible services to help customers address security challenges and is in the process of making many more services HIPAA-eligible. These HIPAA-eligible services (along with all other AWS services) help customers build solutions that comply with HIPAA security and auditing requirements. A catalogue of HIPAA-enabled services can be found at AWS HIPAA-eligible services. It is important to note that AWS manages physical and logical access controls for the AWS boundary. However, the overall security of your workloads is a shared responsibility, where you are responsible for controlling user access to content on your AWS accounts.

AWS storage services allow you to store data efficiently while maintaining high durability and scalability. By using Amazon S3 as the central storage layer, you can take advantage of the Amazon S3 storage management features to get operational metrics on your data sets and transition them between various storage classes to save costs. By tagging objects on Amazon S3, you can build a governance layer on Amazon S3 to grant role based access to objects using Amazon IAM and Amazon S3 bucket policies.

To learn more about the Amazon S3 storage management features, see the following link.

Security

In the example above, we are storing the PHI information in a bucket named “phi.” Now, we want to protect this information to make sure its encrypted, does not have unauthorized access, and all access requests to the data are logged.

Encryption: S3 provides settings to enable default encryption on a bucket. This ensures any object in the bucket is encrypted by default.

Logging: S3 provides object level logging that can be used to capture all API calls to the object. The API calls are logged in cloudtrail for easy access and consolidation. Moreover, it also supports events to proactively alert customers of read and write operations.

Access control: Customers can use S3 bucket policies and IAM policies to restrict access to the phi bucket. It can also put a restriction to enforce multi-factor authentication on the bucket. For example, the following policy enforces multi-factor authentication on the phi bucket:

"Version": "2012-10-17",
    "Id": "123",
    "Statement": [
      {
        "Sid": "",
        "Effect": "Deny",
        "Principal": "*",
        "Action": "s3:*",
        "Resource": "arn:aws:s3:::phi/*",
        "Condition": { "Null": { "aws:MultiFactorAuthAge": true } }
      },
      {
        "Sid": "",
        "Effect": "Allow",
        "Principal": "*",
        "Action": ["s3:GetObject"],
        "Resource": "arn:aws:s3:::phi/*"
      }
    ]
 }

Conclusion

In Part 1 of this blog, we detailed the ingestion, storage, security and management of healthcare data on AWS. Stay tuned for part two where we are going to dive deep into optimizing the data for analytics and machine learning.

Please visit Healthcare Providers and Insurers in the Cloud for additional information.

[1] “Largest Healthcare Data Breaches of 2016.” HIPAA Journal, 29 Aug. 2017
[2] “Largest Healthcare Data Breaches of 2017.” HIPAA Journal, 8 Mar. 2018

About the Author

Stephen Jepsen is a Global HCLS Practice Manager in AWS Professional Services.

Ransomware Update: Viruses Targeting Business IT Servers

2018-04-24 Roderick Bauer

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/ransomware-update-viruses-targeting-business-it-servers/

As ransomware attacks have grown in number in recent months, the tactics and attack vectors also have evolved. While the primary method of attack used to be to target individual computer users within organizations with phishing emails and infected attachments, we’re increasingly seeing attacks that target weaknesses in businesses’ IT infrastructure.

How Ransomware Attacks Typically Work

In our previous posts on ransomware, we described the common vehicles used by hackers to infect organizations with ransomware viruses. Most often, downloaders distribute trojan horses through malicious downloads and spam emails. The emails contain a variety of file attachments, which if opened, will download and run one of the many ransomware variants. Once a user’s computer is infected with a malicious downloader, it will retrieve additional malware, which frequently includes crypto-ransomware. After the files have been encrypted, a ransom payment is demanded of the victim in order to decrypt the files.

What’s Changed With the Latest Ransomware Attacks?

In 2016, a customized ransomware strain called SamSam began attacking the servers in primarily health care institutions. SamSam, unlike more conventional ransomware, is not delivered through downloads or phishing emails. Instead, the attackers behind SamSam use tools to identify unpatched servers running Red Hat’s JBoss enterprise products. Once the attackers have successfully gained entry into one of these servers by exploiting vulnerabilities in JBoss, they use other freely available tools and scripts to collect credentials and gather information on networked computers. Then they deploy their ransomware to encrypt files on these systems before demanding a ransom. Gaining entry to an organization through its IT center rather than its endpoints makes this approach scalable and especially unsettling.

SamSam’s methodology is to scour the Internet searching for accessible and vulnerable JBoss application servers, especially ones used by hospitals. It’s not unlike a burglar rattling doorknobs in a neighborhood to find unlocked homes. When SamSam finds an unlocked home (unpatched server), the software infiltrates the system. It is then free to spread across the company’s network by stealing passwords. As it transverses the network and systems, it encrypts files, preventing access until the victims pay the hackers a ransom, typically between $10,000 and $15,000. The low ransom amount has encouraged some victimized organizations to pay the ransom rather than incur the downtime required to wipe and reinitialize their IT systems.

The success of SamSam is due to its effectiveness rather than its sophistication. SamSam can enter and transverse a network without human intervention. Some organizations are learning too late that securing internet-facing services in their data center from attack is just as important as securing endpoints.

The typical steps in a SamSam ransomware attack are:

1 Attackers gain access to vulnerable server		Attackers exploit vulnerable software or weak/stolen credentials.

2 Attack spreads via remote access tools		Attackers harvest credentials, create SOCKS proxies to tunnel traffic, and abuse RDP to install SamSam on more computers in the network.

3 Ransomware payload deployed		Attackers run batch scripts to execute ransomware on compromised machines.

4 Ransomware demand delivered requiring payment to decrypt files		Demand amounts vary from victim to victim. Relatively low ransom amounts appear to be designed to encourage quick payment decisions.

What all the organizations successfully exploited by SamSam have in common is that they were running unpatched servers that made them vulnerable to SamSam. Some organizations had their endpoints and servers backed up, while others did not. Some of those without backups they could use to recover their systems chose to pay the ransom money.

Timeline of SamSam History and Exploits

Since its appearance in 2016, SamSam has been in the news with many successful incursions into healthcare, business, and government institutions.

March 2016
SamSam appears

SamSam campaign targets vulnerable JBoss servers
Attackers hone in on healthcare organizations specifically, as they’re more likely to have unpatched JBoss machines.

April 2016
SamSam finds new targets

SamSam begins targeting schools and government.
After initial success targeting healthcare, attackers branch out to other sectors.

April 2017
New tactics include RDP

Attackers shift to targeting organizations with exposed RDP connections, and maintain focus on healthcare.
An attack on Erie County Medical Center costs the hospital $10 million over three months of recovery.

January 2018
Municipalities attacked

• Attack on Municipality of Farmington, NM.
• Attack on Hancock Health.

• Attack on Adams Memorial Hospital
• Attack on Allscripts (Electronic Health Records), which includes 180,000 physicians, 2,500 hospitals, and 7.2 million patients’ health records.

February 2018
Attack volume increases

• Attack on Davidson County, NC.
• Attack on Colorado Department of Transportation.

March 2018
SamSam shuts down Atlanta

• Second attack on Colorado Department of Transportation.
• City of Atlanta suffers a devastating attack by SamSam.
The attack has far-reaching impacts — crippling the court system, keeping residents from paying their water bills, limiting vital communications like sewer infrastructure requests, and pushing the Atlanta Police Department to file paper reports.

• SamSam campaign nets $325,000 in 4 weeks.
Infections spike as attackers launch new campaigns. Healthcare and government organizations are once again the primary targets.

How to Defend Against SamSam and Other Ransomware Attacks

The best way to respond to a ransomware attack is to avoid having one in the first place. If you are attacked, making sure your valuable data is backed up and unreachable by ransomware infection will ensure that your downtime and data loss will be minimal or none if you ever suffer an attack.

In our previous post, How to Recover From Ransomware, we listed the ten ways to protect your organization from ransomware.

Use anti-virus and anti-malware software or other security policies to block known payloads from launching.
Make frequent, comprehensive backups of all important files and isolate them from local and open networks. Cybersecurity professionals view data backup and recovery (74% in a recent survey) by far as the most effective solution to respond to a successful ransomware attack.
Keep offline backups of data stored in locations inaccessible from any potentially infected computer, such as disconnected external storage drives or the cloud, which prevents them from being accessed by the ransomware.
Install the latest security updates issued by software vendors of your OS and applications. Remember to patch early and patch often to close known vulnerabilities in operating systems, server software, browsers, and web plugins.
Consider deploying security software to protect endpoints, email servers, and network systems from infection.
Exercise cyber hygiene, such as using caution when opening email attachments and links.
Segment your networks to keep critical computers isolated and to prevent the spread of malware in case of attack. Turn off unneeded network shares.
Turn off admin rights for users who don’t require them. Give users the lowest system permissions they need to do their work.
Restrict write permissions on file servers as much as possible.
Educate yourself, your employees, and your family in best practices to keep malware out of your systems. Update everyone on the latest email phishing scams and human engineering aimed at turning victims into abettors.

Please Tell Us About Your Experiences with Ransomware

Have you endured a ransomware attack or have a strategy to avoid becoming a victim? Please tell us of your experiences in the comments.

The post Ransomware Update: Viruses Targeting Business IT Servers appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Cybersecurity Insurance

2018-04-12 Bruce Schneier

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/04/cybersecurity_i_1.html

Good article about how difficult it is to insure an organization against Internet attacks, and how expensive the insurance is.

Companies like retailers, banks, and healthcare providers began seeking out cyberinsurance in the early 2000s, when states first passed data breach notification laws. But even with 20 years’ worth of experience and claims data in cyberinsurance, underwriters still struggle with how to model and quantify a unique type of risk.
“Typically in insurance we use the past as prediction for the future, and in cyber that’s very difficult to do because no two incidents are alike,” said Lori Bailey, global head of cyberrisk for the Zurich Insurance Group. Twenty years ago, policies dealt primarily with data breaches and third-party liability coverage, like the costs associated with breach class-action lawsuits or settlements. But more recent policies tend to accommodate first-party liability coverage, including costs like online extortion payments, renting temporary facilities during an attack, and lost business due to systems failures, cloud or web hosting provider outages, or even IT configuration errors.

In my new book — out in September — I write:

There are challenges to creating these new insurance products. There are two basic models for insurance. There’s the fire model, where individual houses catch on fire at a fairly steady rate, and the insurance industry can calculate premiums based on that rate. And there’s the flood model, where an infrequent large-scale event affects large numbers of people — but again at a fairly steady rate. Internet+ insurance is complicated because it follows neither of those models but instead has aspects of both: individuals are hacked at a steady (albeit increasing) rate, while class breaks and massive data breaches affect lots of people at once. Also, the constantly changing technology landscape makes it difficult to gather and analyze the historical data necessary to calculate premiums.

BoingBoing article.

Securing messages published to Amazon SNS with AWS PrivateLink

2018-04-10 Otavio Ferreira

Post Syndicated from Otavio Ferreira original https://aws.amazon.com/blogs/security/securing-messages-published-to-amazon-sns-with-aws-privatelink/

Amazon Simple Notification Service (SNS) now supports VPC Endpoints (VPCE) via AWS PrivateLink. You can use VPC Endpoints to privately publish messages to SNS topics, from an Amazon Virtual Private Cloud (VPC), without traversing the public internet. When you use AWS PrivateLink, you don’t need to set up an Internet Gateway (IGW), Network Address Translation (NAT) device, or Virtual Private Network (VPN) connection. You don’t need to use public IP addresses, either.

VPC Endpoints doesn’t require code changes and can bring additional security to Pub/Sub Messaging use cases that rely on SNS. VPC Endpoints helps promote data privacy and is aligned with assurance programs, including the Health Insurance Portability and Accountability Act (HIPAA), FedRAMP, and others discussed below.

VPC Endpoints for SNS in action

Here’s how VPC Endpoints for SNS works. The following example is based on a banking system that processes mortgage applications. This banking system, which has been deployed to a VPC, publishes each mortgage application to an SNS topic. The SNS topic then fans out the mortgage application message to two subscribing AWS Lambda functions:

Save-Mortgage-Application stores the application in an Amazon DynamoDB table. As the mortgage application contains personally identifiable information (PII), the message must not traverse the public internet.
Save-Credit-Report checks the applicant’s credit history against an external Credit Reporting Agency (CRA), then stores the final credit report in an Amazon S3 bucket.

The following diagram depicts the underlying architecture for this banking system:

To protect applicants’ data, the financial institution responsible for developing this banking system needed a mechanism to prevent PII data from traversing the internet when publishing mortgage applications from their VPC to the SNS topic. Therefore, they created a VPC endpoint to enable their publisher Amazon EC2 instance to privately connect to the SNS API. As shown in the diagram, when the VPC endpoint is created, an Elastic Network Interface (ENI) is automatically placed in the same VPC subnet as the publisher EC2 instance. This ENI exposes a private IP address that is used as the entry point for traffic destined to SNS. This ensures that traffic between the VPC and SNS doesn’t leave the Amazon network.

Set up VPC Endpoints for SNS

The process for creating a VPC endpoint to privately connect to SNS doesn’t require code changes: access the VPC Management Console, navigate to the Endpoints section, and create a new Endpoint. Three attributes are required:

The SNS service name.
The VPC and Availability Zones (AZs) from which you’ll publish your messages.
The Security Group (SG) to be associated with the endpoint network interface. The Security Group controls the traffic to the endpoint network interface from resources in your VPC. If you don’t specify a Security Group, the default Security Group for your VPC will be associated.

Help ensure your security and compliance

SNS can support messaging use cases in regulated market segments, such as healthcare provider systems subject to the Health Insurance Portability and Accountability Act (HIPAA) and financial systems subject to the Payment Card Industry Data Security Standard (PCI DSS), and is also in-scope with the following Assurance Programs:

ISO 9001, ISO 27001, ISO 27017, ISO 27018
FedRAMP Moderate (East/West), FedRAMP High (GovCloud)
DoD CC SRG IL2 (East/West/GovCloud), DoD CC SRG IL4 (GovCloud)
SOC 1, SOC 2, SOC 3
HIPAA BAA
FIPS 140-2
PCI DSS
IRAP

The SNS API is served through HTTP Secure (HTTPS), and encrypts all messages in transit with Transport Layer Security (TLS) certificates issued by Amazon Trust Services (ATS). The certificates verify the identity of the SNS API server when encrypted connections are established. The certificates help establish proof that your SNS API client (SDK, CLI) is communicating securely with the SNS API server. A Certificate Authority (CA) issues the certificate to a specific domain. Hence, when a domain presents a certificate that’s issued by a trusted CA, the SNS API client knows it’s safe to make the connection.

Summary

VPC Endpoints can increase the security of your pub/sub messaging use cases by allowing you to publish messages to SNS topics, from instances in your VPC, without traversing the internet. Setting up VPC Endpoints for SNS doesn’t require any code changes because the SNS API address remains the same.

VPC Endpoints for SNS is now available in all AWS Regions where AWS PrivateLink is available. For information on pricing and regional availability, visit the VPC pricing page.
For more information and on-boarding, see Publishing to Amazon SNS Topics from Amazon Virtual Private Cloud in the SNS documentation.

If you have comments about this post, submit them in the Comments section below. If you have questions about anything in this post, start a new thread on the Amazon SNS forum or contact AWS Support.

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How to Use Service Control Policies in AWS Organizations

2018-04-02 Frank Phillis

Post Syndicated from Frank Phillis original https://aws.amazon.com/blogs/security/how-to-use-service-control-policies-in-aws-organizations/

With AWS Organizations, you can centrally manage policies across multiple AWS accounts without having to use custom scripts and manual processes. For example, you can apply service control policies (SCPs) across multiple AWS accounts that are members of an organization. SCPs allow you to define which AWS service APIs can and cannot be executed by AWS Identity and Access Management (IAM) entities (such as IAM users and roles) in your organization’s member AWS accounts. SCPs are created and applied from the master account, which is the AWS account that you used when you created your organization.

In a previous post, How to Use Service Control Policies in AWS Organizations to Enforce Healthcare Compliance in Your AWS Account, we reviewed how to create and manage SCPs and Organizational Units (OU) within an organization. In this post, I show how to use SCPs for access control in Organizations, with a specific focus on evaluating SCPs when an IAM entity calls an API in a member AWS account. I first cover some key Organizations concepts, and then I show how an SCP attached to an organization impacts which AWS service APIs are available to member accounts. Finally, I demonstrate these concepts with an example.

Organizational structure in Organizations

OUs give you a way to logically group and structure member AWS accounts in your organization. The screenshot shows the tree view of an example organizational structure in my organization with several OUs. Currently, I have selected OrgUnit01, and this is the current view I see in my main window. You can see here that within the OrgUnit01 OU, I have nested two additional OUs (OrgUnit01ChildA and OrgUnit01ChildB) and an AWS account is also contained within OrgUnit01, named “Developer Sandbox Account”.

The parts of the example organizational structure in the screenshot are:

Tree view — The hierarchy of your organization’s root and any OUs you have created
Tree view toggle — Enable and disable tree view
Organizational Units — Any child OUs of the selected root or OU in tree view
Accounts — Any AWS accounts (members or master) in the current OU

In the next section, I explain why at least one SCP must be attached to your root and OUs and introduce SCP evaluation.

How Service Control Policy evaluation logic works

To allow an AWS service API at the member account level, you must allow the API at every level between the member account and the root of your organization. This means you must attach an SCP at every level between your organization’s root and the member account that allows the given AWS service API (such as ec2:RunInstances). For more information, see About Service Control Policies.

Let’s say you want to allow the ec2:RunInstances API in the Developer Sandbox Account in the example structure in the preceding screenshot. To allow this AWS service API, you must allow the API in at least one SCP attached at each of these levels:

The organization’s root
The OU named OrgUnit01

If you don’t allow the AWS service in an SCP attached at each of these two levels, neither IAM entities nor the root user in the Developer Sandbox Account will be able to call ec2:RunInstances, even if an administrator has given them permission to do so (for IAM entities). In terms of policy evaluation, SCPs follow exactly the same policy evaluation logic as IAM does: by default, all requests are denied, an explicit allow overrides this default, and an explicit deny overrides any explicit allows.

What does this look like in practice? In the next section, I share a practical example to demonstrate how this works in Organizations.

An example structure with nested OUs and SCPs

In the previous section, I introduced design aspects of AWS Organizations that help prevent administrators from breaking structures in their Organizations. But because AWS Organizations is flexible enough to address multiple use cases, administrators can make changes that have unintended consequences, such as breaking organizational structures when moving an AWS account from one OU to another. In this section, I show an example with broken OU and SCP structures and explain how you can fix them.

I’ll take a blacklisting approach. That is, I’ll use the FullAWSAccess SCP, which doesn’t filter out any AWS service APIs. Then, I will filter out specific APIs by blacklisting them in subsequent SCPs attached to OUs at various points in my organization’s structure. For further reading on blacklisting and whitelisting with AWS Organizations, review AWS Organizations Terminology and Concepts.

Let’s say I have developed the OU and SCP structure shown in the diagram below. Before taking a close look at that diagram, I’ll briefly outline the goals I’m trying to achieve. Broadly speaking, there’s a small subset of APIs that I want to filter out using SCPs. This means that IAM entities in some AWS accounts in my organization will not have access to particular AWS service APIs, such as those related to Amazon EC2, while other accounts will not have access to APIs associated with Amazon CloudWatch, Amazon S3, and so on. Apart from these special cases, I do want the accounts in my organization to have access to all other APIs. More specifically, my goals are as follows:

Any AWS accounts in the Root should not have any API filtered out.
Any AWS accounts in OU 001 should have APIs for CloudWatch filtered out, but all other APIs will be accessible.
Any AWS accounts in OU 002 should have APIs for both CloudWatch and EC2 filtered out, but all other APIs will be accessible.
Any AWS accounts in OU 003 should have APIs for S3 filtered out, but all other APIs will be accessible.

To that end, let’s now look at my initial SCP and OU configuration in the image below that shows the example OU and SCP structure. The arrow shows the direction of inheritance: the root and the OUs below it (children) inherit SCPs from the OUs above them (parents). This example structure contains the following SCPs:

FullAWSAccess — Allows all AWS service APIs
Deny_CW — Denies all CloudWatch APIs
Deny_EC2 — Denies all Amazon EC2 APIs
Deny_S3 — Denies all Amazon S3 APIs

Now that I’ve outlined my intent, and shown you the OU / SCP structure that I’ve created to meet that set of goals, you can probably already see that the structure provided in the image above will not work correctly for my stated goals. In fact, AWS accounts in the Root container and OU 001 will have the intended access, as per my goals (1) and (2). I will not, however, meet my goals (3) and (4) with the above structure: entities in member accounts directly under OU 002 cannot perform any actions, even if they’re granted permissions by IAM access policies. This is because the FullAWSAccess SCP isn’t attached directly to this OU (it’s only inherited).

Why is this important? For an AWS service API to be available to IAM entities in a member account, the API must be specified in an SCP attached at every level all the way down the hierarchy to the relevant member account. Similarly, even though OU 003 does have the FullAWSAccess SCP attached directly to it, the fact that it’s not attached to the parent OU (OU 002) means that IAM entities in member accounts under OU 003 also aren’t able to access any service APIs. This doesn’t happen by default—I have deliberately taken this action to organize my structure in this way, to show both the flexibility and the kind of problems you can encounter when working with OUs and SCPs.

So I now need to fix the problems that I’ve inadvertently created. To start with, I’m going to make one change to OU 002 by attaching the FullAWSAccess SCP directly to that OU. After I do that, OU 002 has the attached and inherited policies that are shown in the following image.

With the FullAWSAccess policy attached to OU 002, member accounts in both OU 002 and OU 003 can access the other non-restricted AWS service APIs (keeping in mind that the FullAWSAccess policy was already applied to OU 003).

I have one final issue to address in this example: OU 003 has an SCP attached that blocks access to the Amazon S3 APIs. However, in this OU, the intent is to allow IAM entities in member accounts to access the EC2 APIs. EC2 API access is blocked because in the parent OU (OU 002), an SCP is attached that denies access to that API (the Deny_EC2 SCP), which means that any actions listed in Deny_EC2 have already been filtered out. An explicit deny always trumps an allow, so to meet goal (4) and have an OU in which EC2 APIs are allowed but access to CloudWatch APIs and S3 APIs is filtered out, I will move OU 003 up one level, placing it directly under OU 001. This change gives me a working OU and policy structure, as shown in the following image.

I recommend that at each level of your organization’s hierarchy, you directly apply the relevant SCPs. By doing this, you’re less likely to forget to apply an SCP to a particular OU, which can break your permission structure. By directly applying SCPs, you also make your policy structure easier to read.

If you have a group of accounts in your organization that are for testing purposes, I recommend that you experiment with OUs and SCPs. Applying SCPs to OUs and then moving an AWS account around within that structure can show you how SCPs affect IAM entities. For example, if you have an IAM user with the AdministratorAccess policy attached, you should see how SCPs can filter out certain AWS service APIs from specified member accounts.

Conclusion

I showed you how you can effectively apply SCPs to OUs in your organization and avoid some of the common issues that you might experience. I demonstrated an approach to designing a working organizational structure that I hope will help smooth your deployment of your organization and enables you to better centrally secure and manage your AWS accounts.

If you have comments about this post, submit them in the Comments section below. If you have questions about anything in this post, start a new thread on the Organizations forum.

Security of Cloud HSMBackups

2018-03-28 Balaji Iyer

Post Syndicated from Balaji Iyer original https://aws.amazon.com/blogs/architecture/security-of-cloud-hsmbackups/

Today, our customers use AWS CloudHSM to meet corporate, contractual and regulatory compliance requirements for data security by using dedicated Hardware Security Module (HSM) instances within the AWS cloud. CloudHSM delivers all the benefits of traditional HSMs including secure generation, storage, and management of cryptographic keys used for data encryption that are controlled and accessible only by you.

As a managed service, it automates time-consuming administrative tasks such as hardware provisioning, software patching, high availability, backups and scaling for your sensitive and regulated workloads in a cost-effective manner. Backup and restore functionality is the core building block enabling scalability, reliability and high availability in CloudHSM.

You should consider using AWS CloudHSM if you require:

Keys stored in dedicated, third-party validated hardware security modules under your exclusive control
FIPS 140-2 compliance
Integration with applications using PKCS#11, Java JCE, or Microsoft CNG interfaces
High-performance in-VPC cryptographic acceleration (bulk crypto)
Financial applications subject to PCI regulations
Healthcare applications subject to HIPAA regulations
Streaming video solutions subject to contractual DRM requirements

We recently released a whitepaper, “Security of CloudHSM Backups” that provides in-depth information on how backups are protected in all three phases of the CloudHSM backup lifecycle process: Creation, Archive, and Restore.

About the Author

Balaji Iyer is a senior consultant in the Professional Services team at Amazon Web Services. In this role, he has helped several customers successfully navigate their journey to AWS. His specialties include architecting and implementing highly-scalable distributed systems, operational security, large scale migrations, and leading strategic AWS initiatives.

Create data science environments on AWS for health analysis using OHDSI

2018-03-23 James Wiggins

Post Syndicated from James Wiggins original https://aws.amazon.com/blogs/big-data/creating-data-science-environments-on-aws-for-health-analysis-using-ohdsi/

Applying technology to healthcare data has the potential to produce many exciting and important outcomes. The analysis produced from healthcare data can empower clinicians to improve the health of individuals and populations by enabling them to make better decisions that enhance the care they provide.

The Observational Health Data Sciences and Informatics (OHDSI, pronounced “Odyssey”) program and community is working toward this goal by producing data standards and open-source solutions to store and analyze observational health data. Using the OHDSI tools, you can visualize the health of your entire population. You can build cohorts of patients, analyze incidence rates for various conditions, and estimate the effect of treatments on patients with certain conditions. You can also model health outcome predictions using machine learning algorithms.

One of the challenges often faced when working with big data tools is the expense of the infrastructure required to run them. Another challenge is the learning curve to implement and begin using these tools. Amazon Web Services has enabled us to address many of the classic IT challenges by making enterprise class infrastructure and technology available in an affordable, elastic, and automated way. This blog post demonstrates how to combine some of the OHDSI projects (Atlas, Achilles, WebAPI, and the OMOP Common Data Model) with AWS technologies. By doing so, you can quickly and inexpensively implement a health data science and informatics environment.

Shown following is just one example of the population health analysis that is possible with the OHDSI tools. This visualization shows the prevalence of various drugs within the given population of people. This information helps researchers and clinicians discover trends and make better informed decisions about patient health.

OHDSI application architecture on AWS

Before deploying an application on AWS that transmits, processes, or stores protected health information (PHI) or personally identifiable information (PII), address your organization’s compliance concerns. Make sure that you have worked with your internal compliance and legal team to ensure compliance with the laws and regulations that govern your organization. To understand how you can use AWS services as a part of your overall compliance program, see the AWS HIPAA Compliance whitepaper. With that said, we paid careful attention to the HIPAA control set during the design of this solution.

This blog post presents a complete OHDSI application environment, including a data warehouse with sample data. It has the following features:

It’s deployed in an isolated, three-tier Amazon Virtual Private Cloud (Amazon VPC) with high availability
It uses data-at-rest and in-flight encryption (certificates must be added for the web application servers and load balancer)
It uses managed services from AWS; OS, middleware, and database patching and maintenance is largely automatic
It creates automated backups for operational and disaster recovery
It’s built automatically in about an hour
It produces a reasonable monthly cost with Business Level support based on the AWS Solution Calculator

Following, you can see a block diagram of how the OHDSI tools map to the services provided by AWS.

Atlas is the web application that researchers interact with to perform analysis. Atlas interacts with the underlying databases through a web services application named WebAPI. In this example, both Atlas and WebAPI are deployed and managed by AWS Elastic Beanstalk. Elastic Beanstalk is an easy-to-use service for deploying and scaling web applications. Simply upload the Atlas and WebAPI code and Elastic Beanstalk automatically handles the deployment. It covers everything from capacity provisioning, load balancing, autoscaling, and high availability, to application health monitoring. Using a feature of Elastic Beanstalk called ebextensions, the Atlas and WebAPI servers are customized to use an encrypted storage volume for the middleware application logs.

Atlas stores the state of the various patient cohorts that are analyzed in a dedicated database separate from your observational health data. This database is provided by Amazon Aurora with PostgreSQL compatibility.

Amazon Aurora is a relational database built for the cloud that combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. It provides cost-efficient and resizable capacity while automating time-consuming administration tasks such as hardware provisioning, database setup, patching, and backups. It is configured for high availability and uses encryption at rest for the database and backups, and encryption in flight for the JDBC connections.

We accomplish this through the AWS CloudFormation code shown following:

RDSCluster:
    Type: AWS::RDS::DBCluster
    Properties:
      Engine: aurora-postgresql
      StorageEncrypted: 'True'
...
RDSDBClusterParameterGroup:
    Type: AWS::RDS::DBClusterParameterGroup
    Properties:
      Parameters:
        rds.force_ssl: 1
...
RedshiftCluster: 
    Type: "AWS::Redshift::Cluster"
    Properties:
      Encrypted: "True"
      PubliclyAccessible: "False"
...
RedshiftClusterParameterGroup: 
    Type: "AWS::Redshift::ClusterParameterGroup"
    Properties: 
      Parameters: 
        - 
          ParameterName: "require_ssl"
          ParameterValue: "true"

All of your observational health data is stored inside the OHDSI Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). This model also stores useful vocabulary tables that help to translate values from various data sources (like EHR systems and claims data).

The OMOP CDM schema is deployed onto Amazon Redshift. Amazon Redshift is a fast, fully managed data warehouse that allows you to run complex analytic queries against petabytes of structured data. It uses using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution. You can also resize an Amazon Redshift cluster as your requirements for it change.

The solution in this blog post automatically loads de-identified sample data of 1,000 people from the CMS 2008–2010 Data Entrepreneurs’ Synthetic Public Use File (DE-SynPUF). The data has helpful formatting from LTS Computing LLC. Vocabulary data from the OHDSI Athena project is also loaded into the OMOP CDM, and a results set is computed by OHDSI Achilles.

Application Component	AWS Service	Source Link
Automation	AWS CloudFormation	https://github.com/awslabs/aws-ohdsi-automated-deployment
Atlas Application	AWS Elastic Beanstalk	https://github.com/OHDSI/Atlas
WebAPI Web Services	AWS Elastic Beanstalk	https://github.com/OHDSI/WebAPI
WebAPI Database Schema	Amazon Relational Database Service (Amazon RDS) Aurora PostgreSQL	http://www.ohdsi.org/web/wiki/doku.php?id=documentation:software:webapi:postgresql_installation_guide
OMOP CDM v5.2 Database Schema	Amazon Redshift	https://github.com/OHDSI/CommonDataModel/tree/master/Redshift
CMS DE-SynPUF Sample Data	Amazon Redshift	https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/SynPUFs/DE_Syn_PUF.html
Athena Vocabulary Data	Amazon Redshift	http://athena.ohdsi.org/
Achilles Results Computation	Amazon Redshift	https://github.com/OHDSI/Achilles

Following is a detailed technical diagram showing the configuration of the architecture to be deployed.

Deploying OHDSI on AWS

Everything just described is automatically deployed by using an AWS CloudFormation template. Using this template, you can quickly get started with the OHDSI project. The CloudFormation templates for this deployment as well as all of the supporting scripts and source code can be found in the AWS Labs GitHub repo.

From your AWS account, open the CloudFormation Management Console and choose Create Stack. From there, copy and paste the following URL in the Specify an Amazon S3 template URL box, and choose Next.

https://s3.amazonaws.com/aws-bigdata-blog/artifacts/aws-ohdsi-automated-deployment/OHDSI-master.yaml

On the next screen, you provide a Stack Name (this can be anything you like) and a few other parameters for your OHDSI environment.

You use the DatabasePassword parameter to set the password for the master user account of the Amazon Redshift and Aurora databases.

You use the EBEndpoint name to generate a unique URL for Atlas to access the OHDSI environment. It is http://EBEndpoint.AWS-Region.elasticbeanstalk.com, where EBEndpoint.AWS-Region indicates the Elastic Beanstalk endpoint and AWS Region. You can configure this URL through Elastic Beanstalk if you want to change it in the future.

You use the KPair option to choose one of your existing Amazon EC2 key pairs to use with the instances that Elastic Beanstalk deploys. By doing this, you can gain administrative access to these instances in the future if you need to. If you don’t already have an Amazon EC2 key pair, you can generate one for free. You do this by going to the Key Pairs section of the EC2 console and choosing Generate Key Pair.

Finally, you use the UserIPRange parameter to specify a CIDR IP address range from which to access your OHDSI environment. By default, your OHDSI environment is accessible over the public internet. Use UserIPRange to limit access over the Internet to a single IP address or a range of IP addresses that represent users you want to have access. Through additional configuration, you can also make your OHDSI environment completely private and accessible only through a VPN or AWS Direct Connect private circuit.

When you’ve provided all Parameters, choose Next.

On the next screen, you can provide some other optional information like tags at your discretion, or just choose Next.

On the next screen, you can review what will be deployed. At the bottom of the screen, there is a check box for you to acknowledge that AWS CloudFormation might create IAM resources with custom names. This is correct; the template being deployed creates four custom roles that give permission for the AWS services involved to communicate with each other. Details of these permissions are inside the CloudFormation template referenced in the URL given in the first step. Check the box acknowledging this and choose Next.

You can watch as CloudFormation builds out your OHDSI architecture. A CloudFormation deployment is called a stack. The parent stack creates two child stacks, one containing the VPC and IAM roles and another created by Elastic Beanstalk with the Atlas and WebAPI servers. When all three stacks have reached the green CREATE_COMPLETE status, as shown in the screenshot following, then the OHDSI architecture has been deployed.

There is still some work going on behind the scenes, though. To watch the progress, browse to the Amazon Redshift section of your AWS Management Console and choose the Amazon Redshift cluster that was created for your OHDSI architecture. After you do so, you can observe the Loads and Queries tabs.

First, on the Loads tab, you can see the CMS De-SynPUF sample data and Athena vocabulary data being loaded into the OMOP Common Data Model. After you see the VOCABULARY table reach the COMPLETED status (as shown following), all of the sample and vocabulary data has been loaded.

After the data loads, the Achilles computation starts. On the Queries tab, you can watch Achilles running queries against your database to build out the Results schema. Achilles runs a large number of queries, and the entire process can take quite some time (about 20 minutes for the sample data we’ve loaded). Eventually, no new queries show up in the Queries tab, which shows that the Achilles computation is completed. The entire process from the time you executed the CloudFormation template until the Achilles computation is completed usually takes about an hour and 15 minutes.

At this point, you can browse to the Elastic Beanstalk section of the AWS Management Console. There, you can choose the OHDSI Application and Environment (green box) that was deployed by the CloudFormation template. At the top of the dashboard, as shown following, you see a link to a URL. This URL matches the name you provided in the EBEndpoint parameter of the CloudFormation template. Choose this URL, and you can start using Atlas to explore the CMS DE-SynPUF sample data!

Cost of deploying this environment

It used to be common to see healthcare data analytics environments deployed in an on-premises data center with expensive data warehouse appliances and virtualized environments. The cloud era has democratized the availability of the infrastructure required to do this type of data analysis, so that now it is within reach of even small organizations. This environment can expand to analyze petabyte-scale health data, and you only pay for what you need. See an estimated breakdown of the monthly cost components for this environment as deployed on the AWS Solution Calculator.

It’s also worth noting that this environment does not have to be run all of the time. If you are only performing analyses periodically, you can terminate the environment when you are finished and restore it from the database backups when you want to continue working. This would reduce the cost of operation even further.

Summary

Now that you have a fully functional OHDSI environment with sample data, you can use this to explore and learn the toolset and its capabilities. After learning with the sample data, you can begin gaining insights by analyzing your own organization’s health data. You can do this using an extract, transform, load (ETL) process from one or more of your health data sources.

Additional Reading

If you found this post useful, be sure to check out Build a Healthcare Data Warehouse Using Amazon EMR, Amazon Redshift, AWS Lambda, and OMOP for info on how to automate data ETL to the OMOP CDM.

About the Author

James Wiggins is a senior healthcare solutions architect at AWS. He is passionate about using technology to help organizations positively impact world health. He also loves spending time with his wife and three children.

Wanted: Office Administrator

2018-03-19 Yev

Post Syndicated from Yev original https://www.backblaze.com/blog/wanted-office-administrator-2/

At inception, Backblaze was a consumer company. Thousands upon thousands of individuals came to our website and gave us $5/mo to keep their data safe. But, we didn’t sell business solutions. It took us years before we had a sales team. In the last couple of years, we’ve released products that businesses of all sizes love: Backblaze B2 Cloud Storage and Backblaze for Business Computer Backup. Those businesses want to integrate Backblaze into their infrastructure, so it’s time to expand our teams!

Company Description:
Founded in 2007, Backblaze started with a mission to make backup software elegant and provide complete peace of mind. Over the course of almost a decade, we have become a pioneer in robust, scalable low cost cloud backup. Recently, we launched B2 – robust and reliable object storage at just $0.005/gb/mo. Part of our differentiation is being able to offer the lowest price of any of the big players while still being profitable.

We’ve managed to nurture a team oriented culture with amazingly low turnover. We value our people and their families. Don’t forget to check out our “About Us” page to learn more about the people and some of our perks.

We have built a profitable, high growth business. While we love our investors, we have maintained control over the business. That means our corporate goals are simple – grow sustainably and profitably.

Some Backblaze Perks:

Competitive healthcare plans
Competitive compensation and 401k
All employees receive Option grants
Unlimited vacation days
Strong coffee
Fully stocked Micro kitchen
Catered breakfast and lunches
Awesome people who work on awesome projects
New Parent Childcare bonus
Normal work hours
Get to bring your pets into the office
San Mateo Office – located near Caltrain and Highways 101 & 280.

Want to know what you’ll be doing?

You will play a pivotal role at Backblaze! You will be the glue that binds people together in the office and one of the main engines that keeps our company running. This is an exciting opportunity to help shape the company culture of Backblaze by making the office a fun and welcoming place to work. As an Office Administrator, your priority is to help employees have what they need to feel happy, comfortable, and productive at work; whether it’s refilling snacks, collecting shipments, responding to maintenance requests, ordering office supplies, or assisting with fun social events, your contributions will be critical to our culture.

Office Administrator Responsibilities:

Maintain a clean, well-stocked and organized office
Greet visitors and callers, route and resolve information requests
Ensure conference rooms and kitchen areas are clean and stocked
Sign for all packages delivered to the office as well as forward relevant departments
Administrative duties as assigned

Facilities Coordinator Responsibilities:

Act as point of contact for building facilities and other office vendors and deliveries
Work with HR to ensure new hires are welcomed successfully at Backblaze – to include desk/equipment orders, seat planning, and general facilities preparation
Work with the “Fun Committee” to support office events and activities
Be available after hours as required for ongoing business success (events, building issues)

Jr. Buyer Responsibilities:

Assist with creating purchase orders and buying equipment
Compare costs and maintain vendor cards in Quickbooks
Assist with booking travel, hotel accommodations, and conference rooms
Maintain accurate records of purchases and tracking orders
Maintain office equipment, physical space, and maintenance schedules
Manage company calendar, snack, and meal orders

Qualifications:

1 year experience in an Inventory/Shipping/Receiving/Admin role preferred
Proficiency with Microsoft Office applications, Google Apps, Quickbooks, Excel
Experience and skill at adhering to a budget
High attention to detail
Proven ability to prioritize within a multi-tasking environment; highly organized
Collaborative and communicative
Hands-on, “can do” attitude
Personable and approachable
Able to lift up to 50 lbs
Strong data entry

This position is located in San Mateo, California. Backblaze is an Equal Opportunity Employer.

If this all sounds like you:

Send an email to [email protected] with the position in the subject line.
Tell us a bit about your work history.
Include your resume.

The post Wanted: Office Administrator appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Wanted: Vault Storage Engineer

2018-03-16 Yev

Post Syndicated from Yev original https://www.backblaze.com/blog/wanted-vault-storage-engineer/

Want to work at a company that helps customers in 156 countries around the world protect the memories they hold dear? A company that stores over 500 petabytes of customers’ photos, music, documents and work files in a purpose-built cloud storage system?

Well here’s your chance. Backblaze is looking for a Vault Storage Engineer!

Company Description:

Founded in 2007, Backblaze started with a mission to make backup software elegant and provide complete peace of mind. Over the course of almost a decade, we have become a pioneer in robust, scalable low cost cloud backup. Recently, we launched B2 — robust and reliable object storage at just $0.005/gb/mo. Part of our differentiation is being able to offer the lowest price of any of the big players while still being profitable.

Some Backblaze Perks:

Competitive healthcare plans
Competitive compensation and 401k
All employees receive Option grants
Unlimited vacation days
Strong coffee
Fully stocked Micro kitchen
Catered breakfast and lunches
Awesome people who work on awesome projects
New Parent Childcare bonus
Normal work hours
Get to bring your pets into the office
San Mateo Office – located near Caltrain and Highways 101 & 280.

Want to know what you’ll be doing?

You will work on the core of the Backblaze: the vault cloud storage system (https://www.backblaze.com/blog/vault-cloud-storage-architecture/). The system accepts files uploaded from customers, stores them durably by distributing them across the data center, automatically handles drive failures, rebuilds data when drives are replaced, and maintains high availability for customers to download their files. There are significant enhancements in the works, and you’ll be a part of making them happen.

Must have a strong background in:

Computer Science
Multi-threaded programming
Distributed Systems
Java
Math (such as matrix algebra and statistics)
Building reliable, testable systems

Bonus points for:

Java
JavaScript
Python
Cassandra
SQL

Looking for an attitude of:

Passionate about building reliable clean interfaces and systems.
Likes to work closely with other engineers, support, and sales to help customers.
Customer Focused (!!) — always focus on the customer’s point of view and how to solve their problem!

Required for all Backblaze Employees:

Good attitude and willingness to do whatever it takes to get the job done
Strong desire to work for a small fast-paced company
Desire to learn and adapt to rapidly changing technologies and work environment
Rigorous adherence to best practices
Relentless attention to detail
Excellent interpersonal skills and good oral/written communication
Excellent troubleshooting and problem solving skills

This position is located in San Mateo, California but will also consider remote work as long as you’re no more than three time zones away and can come to San Mateo now and then.

Backblaze is an Equal Opportunity Employer.

Contact Us:
If this sounds like you, follow these steps:

Send an email to jobscontact@backblaze.com with the position in the subject line.
Include your resume.
Tell us a bit about your programming experience.

The post Wanted: Vault Storage Engineer appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Needed: Software Engineering Director

2018-03-16 Yev

Post Syndicated from Yev original https://www.backblaze.com/blog/needed-software-engineering-director/

Company Description:

Founded in 2007, Backblaze started with a mission to make backup software elegant and provide complete peace of mind. Over the course of almost a decade, we have become a pioneer in robust, scalable low cost cloud backup. Recently, we launched B2, robust and reliable object storage at just $0.005/gb/mo. We offer the lowest price of any of the big players and are still profitable.

Backblaze has a culture of openness. The hardware designs for our storage pods are open source. Key parts of the software, including the Reed-Solomon erasure coding are open-source. Backblaze is the only company that publishes hard drive reliability statistics.

We’ve managed to nurture a team-oriented culture with amazingly low turnover. We value our people and their families. The team is distributed across the U.S., but we work in Pacific Time, so work is limited to work time, leaving evenings and weekends open for personal and family time. Check out our “About Us” page to learn more about the people and some of our perks.

Our engineering team is 10 software engineers, and 2 quality assurance engineers. Most engineers are experienced, and a couple are more junior. The team will be growing as the company grows to meet the demand for our products; we plan to add at least 6 more engineers in 2018. The software includes the storage systems that run in the data center, the web APIs that clients access, the web site, and client programs that run on phones, tablets, and computers.

The Job:

As the Director of Engineering, you will be:

managing the software engineering team
ensuring consistent delivery of top-quality services to our customers
collaborating closely with the operations team
directing engineering execution to scale the business and build new services
transforming a self-directed, scrappy startup team into a mid-size engineering organization

A successful director will have the opportunity to grow into the role of VP of Engineering. Backblaze expects to continue our exponential growth of our storage services in the upcoming years, with matching growth in the engineering team..

This position is located in San Mateo, California.

Qualifications:

We are a looking for a director who:

has a good understanding of software engineering best practices
has experience scaling a large, distributed system
gets energized by creating an environment where engineers thrive
understands the trade-offs between building a solid foundation and shipping new features
has a track record of building effective teams

Required for all Backblaze Employees:

Good attitude and willingness to do whatever it takes to get the job done
Strong desire to work for a small fast-paced company
Desire to learn and adapt to rapidly changing technologies and work environment
Rigorous adherence to best practices
Relentless attention to detail
Excellent interpersonal skills and good oral/written communication
Excellent troubleshooting and problem solving skills

Some Backblaze Perks:

Competitive healthcare plans
Competitive compensation and 401k
All employees receive Option grants
Unlimited vacation days
Strong coffee
Fully stocked Micro kitchen
Catered breakfast and lunches
Awesome people who work on awesome projects
New Parent Childcare bonus
Normal work hours
Get to bring your pets into the office
San Mateo Office — located near Caltrain and Highways 101 & 280.

Contact Us:

If this sounds like you, follow these steps:

Send an email to jobscontact@backblaze.com with the position in the subject line.
Include your resume.
Tell us a bit about your experience.

Backblaze is an Equal Opportunity Employer.

The post Needed: Software Engineering Director appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Needed: Senior Software Engineer

2018-03-13 Yev

Post Syndicated from Yev original https://www.backblaze.com/blog/needed-senior-software-engineer/

Well, here’s your chance. Backblaze is looking for a Sr. Software Engineer!

Company Description:

Founded in 2007, Backblaze started with a mission to make backup software elegant and provide complete peace of mind. Over the course of almost a decade, we have become a pioneer in robust, scalable low cost cloud backup. Recently, we launched B2 – robust and reliable object storage at just $0.005/gb/mo. Part of our differentiation is being able to offer the lowest price of any of the big players while still being profitable.

Some Backblaze Perks:

Competitive healthcare plans
Competitive compensation and 401k
All employees receive Option grants
Unlimited vacation days
Strong coffee
Fully stocked Micro kitchen
Catered breakfast and lunches
Awesome people who work on awesome projects
New Parent Childcare bonus
Normal work hours
Get to bring your pets into the office
San Mateo Office – located near Caltrain and Highways 101 & 280

Want to know what you’ll be doing?

You will work on the server side APIs that authenticate users when they log in, accept the backups, manage the data, and prepare restored data for customers. And you will help build new features as well as support tools to help chase down and diagnose customer issues.

Must be proficient in:

Java
Apache Tomcat
Large scale systems supporting thousands of servers and millions of customers
Cross platform (Linux/Macintosh/Windows) — don’t need to be an expert on all three, but cannot be afraid of any

Bonus points for:

Cassandra experience
JavaScript
ReactJS
Python
Struts
JSP’s

Looking for an attitude of:

Passionate about building friendly, easy to use Interfaces and APIs.
Likes to work closely with other engineers, support, and sales to help customers.
Believes the whole world needs backup, not just English speakers in the USA.
Customer Focused (!!) — always focus on the customer’s point of view and how to solve their problem!

Required for all Backblaze Employees:

Good attitude and willingness to do whatever it takes to get the job done
Strong desire to work for a small, fast-paced company
Desire to learn and adapt to rapidly changing technologies and work environment
Rigorous adherence to best practices
Relentless attention to detail
Excellent interpersonal skills and good oral/written communication
Excellent troubleshooting and problem solving skills

This position is located in San Mateo, California but will also consider remote work as long as you’re no more than three time zones away and can come to San Mateo now and then.

Backblaze is an Equal Opportunity Employer.

If this sounds like you —follow these steps:

Send an email to [email protected] with the position in the subject line.
Include your resume.
Tell us a bit about your programming experience.

The post Needed: Senior Software Engineer appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Needed: Sales Development Representative!

2018-03-08 Yev

Post Syndicated from Yev original https://www.backblaze.com/blog/needed-sales-development-representative/

Company Description:
Founded in 2007, Backblaze started with a mission to make backup software elegant and provide complete peace of mind. Over the course of almost a decade, we have become a pioneer in robust, scalable low cost cloud backup. Recently, we launched B2 — robust and reliable object storage at just $0.005/gb/mo. Part of our differentiation is being able to offer the lowest price of any of the big players while still being profitable.

We have built a profitable, high growth business. While we love our investors, we have maintained control over the business. That means our corporate goals are simple — grow sustainably and profitably.

Some Backblaze Perks:

Competitive healthcare plans
Competitive compensation and 401k
All employees receive option grants
Unlimited vacation days
Strong coffee
Fully stocked Micro kitchen
Catered breakfast and lunches
Awesome people who work on awesome projects
New Parent Childcare bonus
Normal work hours
Get to bring your pets into the office
San Mateo Office — located near Caltrain and Highways 101 & 280

As our first Sales Development Representative (SDR), we are looking for someone who is organized, has high-energy and strong interpersonal communication skills. The ideal person will have a passion for sales, love to cold call and figure out new ways to get potential customers. Ideally the SDR will have 1-2 years experience working in a fast paced sales environment. We are looking for someone who knows how to manage their time and has top class communication skills. It’s critical that our SDR is able to learn quickly when using new tools.

Additional Responsibilities Include:

Generate qualified leads, set up demos and outbound opportunities by phone and email.
Work with our account managers to pass qualified leads and track in salesforce.com.
Report internally on prospecting performance and identify potential optimizations.
Continuously fine tune outbound messaging – both email and cold calls to drive results.
Update and leverage salesforce.com and other sales tools to better track business and drive efficiencies.

Qualifications:

Bachelor’s degree (B.A.)
Minimum of 1-2 years of sales experience.
Excellent written and verbal communication skills.
Proven ability to work in a fast-paced, dynamic and goal-oriented environment.
Maintain a high sense of urgency and entrepreneurial work ethic that is required to drive business outcomes, with exceptional attention to detail.
Positive“can do” attitude, passionate and able to show commitment.
Fearless yet cordial personality- not afraid to make cold calls and introductions yet personable enough to connect with potential Backblaze customers.
Articulate and good listening skills.
Ability to set and manage multiple priorities.

What’s it like working with the Sales team?

The Backblaze sales team collaborates. We help each other out by sharing ideas, templates, and our customer’s experiences. When we talk about our accomplishments, there is no “I did this,” only “we.” We are truly a team.

We are honest to each other and our customers and communicate openly. We aim to have fun by embracing crazy ideas and creative solutions. We try to think not outside the box, but with no boxes at all. Customers are the driving force behind the success of the company and we care deeply about their success.

If this all sounds like you:

Send an email to jobscontact@backblaze.com with the position in the subject line.
Tell us a bit about your sales experience.
Include your resume.

The post Needed: Sales Development Representative! appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Wanted: Senior Systems Administrator

2018-02-28 Yev

Post Syndicated from Yev original https://www.backblaze.com/blog/wanted-senior-systems-administrator/

We’re looking for someone who enjoys solving difficult problems, running down elusive tech gremlins, and improving our environment one server at a time. If you enjoy being stretched, learning new skills, and want to look forward to seeing your co-workers every day, then we want you!

Backblaze is a small (in headcount) cloud storage (and backup!) company with a big mission, bringing feature-rich and accessible services to the masses, even if they don’t have unlimited VC funding (because we don’t either)! We believe in a fun and positive work environment where people can learn and grow, and where a sense of community is not just a buzzword from a company handbook (though you might probably find it in there).

What You’ll Be Doing

Mastering your craft, becoming a subject matter expert, and acting as an escalation point for areas of expertise (this means responding to pages in your areas of ownership as well)
Leading projects across a range of IT operations disciplines
Developing a thorough understanding of the environment and the skills necessary to troubleshoot all systems and services
Collaborating closely with other teams (Engineering, Infrastructure, etc.) to build out new systems and improve existing ones
Participating in on-call rotation when necessary
Petting the office dogs when appropriate

What You Should Have

5+ years of work as a Systems Administrator (or equivalent college degree)
Expert knowledge of Linux systems administration (Debian preferred)
Ability to work under pressure in a fast-paced startup environment
A passion for build and improving all manner of systems and services
Excellent problem solving, investigative, and troubleshooting skills
Strong interpersonal communication skills
Local enough to commute to San Mateo office

Highly Desirable Skills

Experience working at a technology/software startup
Configuration management and automation software (Ansible preferred)
Familiarity with server and storage system hardware and configurations
Understanding of Java servlet containers (Tomcat preferred)
Skill in administration of different software suites and cloud-based integrations (G Suite, PagerDuty, etc.)
Comprehension of standard web services and packages (WordPress, Apache, etc.)

Some Backblaze Perks

Generous healthcare plans
Competitive compensation and 401k
All employees receive Option grants
Unlimited vacation days
Strong coffee
Fully stocked Micro kitchens
Weekly catered breakfast and lunches
Awesome people who work on awesome projects
Childcare bonus (human children only)
Get to bring your (well behaved) pets into the office
Backblaze is an Equal Opportunity Employer and we offer competitive salary and benefits, including our no policy vacation policy

If this sounds like you — follow these steps:

Send an email to jobscontact@backblaze.com with the position in the subject line.
Include your resume.
Tell us a bit about your experience and why you’re excited to work with Backblaze.

The post Wanted: Senior Systems Administrator appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Early Challenges: Making Critical Hires

2018-02-13 Gleb Budman

Post Syndicated from Gleb Budman original https://www.backblaze.com/blog/early-challenges-making-critical-hires/

This post by Backblaze’s CEO and co-founder Gleb Budman is the ninth in a series about entrepreneurship. You can choose posts in the series from the list below:

Use the Join button above to receive notification of new posts in this series.

In 2009, Google disclosed that they had 400 recruiters on staff working to hire nearly 10,000 people. Someday, that might be your challenge, but most companies in their early days are looking to hire a handful of people — the right people — each year. Assuming you are closer to startup stage than Google stage, let’s look at who you need to hire, when to hire them, where to find them (and how to help them find you), and how to get them to join your company.

Who Should Be Your First Hires

In later stage companies, the roles in the company have been well fleshed out, don’t change often, and each role can be segmented to focus on a specific area. A large company may have an entire department focused on just cubicle layout; at a smaller company you may not have a single person whose actual job encompasses all of facilities. At Backblaze, our CTO has a passion and knack for facilities and mostly led that charge. Also, the needs of a smaller company are quick to change. One of our first hires was a QA person, Sean, who ended up being 100% focused on data center infrastructure. In the early stage, things can shift quite a bit and you need people that are broadly capable, flexible, and most of all willing to pitch in where needed.

That said, there are times you may need an expert. At a previous company we hired Jon, a PhD in Bayesian statistics, because we needed algorithmic analysis for spam fighting. However, even that person was not only able and willing to do the math, but also code, and to not only focus on Bayesian statistics but explore a plethora of spam fighting options.

When To Hire

If you’ve raised a lot of cash and are willing to burn it with mistakes, you can guess at all the roles you might need and start hiring for them. No judgement: that’s a reasonable strategy if you’re cash-rich and time-poor.

If your cash is limited, try to see what you and your team are already doing and then hire people to take those jobs. It may sound counterintuitive, but if you’re already doing it presumably it needs to be done, you have a good sense of the type of skills required to do it, and you can bring someone on-board and get them up to speed quickly. That then frees you up to focus on tasks that can’t be done by someone else. At Backblaze, I ran marketing internally for years before hiring a VP of Marketing, making it easier for me to know what we needed. Once I was hiring, my primary goal was to find someone I could trust to take that role completely off of me so I could focus solely on my CEO duties

Where To Find the Right People

Finding great people is always difficult, particularly when the skillsets you’re looking for are highly in-demand by larger companies with lots of cash and cachet. You, however, have one massive advantage: you need to hire 5 people, not 5,000.

People You Worked With

The absolutely best people to hire are ones you’ve worked with before that you already know are good in a work situation. Consider your last job, the one before, and the one before that. A significant number of the people we recruited at Backblaze came from our previous startup MailFrontier. We knew what they could do and how they would fit into the culture, and they knew us and thus could quickly meld into the environment. If you didn’t have a previous job, consider people you went to school with or perhaps individuals with whom you’ve done projects previously.

People You Know

Hiring friends, family, and others can be risky, but should be considered. Sometimes a friend can be a “great buddy,” but is not able to do the job or isn’t a good fit for the organization. Having to let go of someone who is a friend or family member can be rough. Have the conversation up front with them about that possibility, so you have the ability to stay friends if the position doesn’t work out. Having said that, if you get along with someone as a friend, that’s one critical component of succeeding together at work. At Backblaze we’ve hired a number of people successfully that were friends of someone in the organization.

Friends Of People You Know

Your network is likely larger than you imagine. Your employees, investors, advisors, spouses, friends, and other folks all know people who might be a great fit for you. Make sure they know the roles you’re hiring for and ask them if they know anyone that would fit. Search LinkedIn for the titles you’re looking for and see who comes up; if they’re a 2nd degree connection, ask your connection for an introduction.

People You Know About

Sometimes the person you want isn’t someone anyone knows, but you may have read something they wrote, used a product they’ve built, or seen a video of a presentation they gave. Reach out. You may get a great hire: worst case, you’ll let them know they were appreciated, and make them aware of your organization.

Other Places to Find People

There are a million other places to find people, including job sites, community groups, Facebook/Twitter, GitHub, and more. Consider where the people you’re looking for are likely to congregate online and in person.

A Comment on Diversity

Hiring “People You Know” can often result in “Hiring People Like You” with the same workplace experiences, culture, background, and perceptions. Some studies have shown [1, 2, 3, 4] that homogeneous groups deliver faster, while heterogeneous groups are more creative. Also, “Hiring People Like You” often propagates the lack of women and minorities in tech and leadership positions in general. When looking for people you know, keep an eye to not discount people you know who don’t have the same cultural background as you.

Helping People To Find You

Reaching out proactively to people is the most direct way to find someone, but you want potential hires coming to you as well. To do this, they have to a) be aware of you, b) know you have a role they’re interested in, and c) think they would want to work there. Let’s tackle a) and b) first below.

Your Blog

I started writing our blog before we launched the product and talked about anything I found interesting related to our space. For several years now our team has owned the content on the blog and in 2017 over 1.5 million people read it. Each time we have a position open it’s published to the blog. If someone finds reading about backup and storage interesting, perhaps they’d want to dig in deeper from the inside. Many of the people we’ve recruited have mentioned reading the blog as either how they found us or as a factor in why they wanted to work here.
[BTW, this is Gleb’s 200th post on Backblaze’s blog. The first was in 2008. — Editor]

Your Email List

In addition to the emails our blog subscribers receive, we send regular emails to our customers, partners, and prospects. These are largely focused on content we think is directly useful or interesting for them. However, once every few months we include a small mention that we’re hiring, and the positions we’re looking for. Often a small blurb is all you need to capture people’s imaginations whether they might find the jobs interesting or can think of someone that might fit the bill.

Your Social Involvement

Whether it’s Twitter or Facebook, Hacker News or Slashdot, your potential hires are engaging in various communities. Being socially involved helps make people aware of you, reminds them of you when they’re considering a job, and paints a picture of what working with you and your company would be like. Adam was in a Reddit thread where we were discussing our Storage Pods, and that interaction was ultimately part of the reason he left Apple to come to Backblaze.

Convincing People To Join

Once you’ve found someone or they’ve found you, how do you convince them to join? They may be currently employed, have other offers, or have to relocate. Again, while the biggest companies have a number of advantages, you might have more unique advantages than you realize.

Why Should They Join You

Here are a set of items that you may be able to offer which larger organizations might not:

Role: Consider the strengths of the role. Perhaps it will have broader scope? More visibility at the executive level? No micromanagement? Ability to take risks? Option to create their own role?

Compensation: In addition to salary, will their options potentially be worth more since they’re getting in early? Can they trade-off salary for more options? Do they get option refreshes?

Benefits: In addition to healthcare, food, and 401(k) plans, are there unique benefits of your company? One company I knew took the entire team for a one-month working retreat abroad each year.

Location: Most people prefer to work close to home. If you’re located outside of the San Francisco Bay Area, you might be at a disadvantage for not being in the heart of tech. But if you find employees close to you you’ve got a huge advantage. Sometimes it’s micro; even in the Bay Area the difference of 5 miles can save 20 minutes each way every day. We located the Backblaze headquarters in San Mateo, a middle-ground that made it accessible to those coming from San Jose and San Francisco. We also chose a downtown location near a train, restaurants, and cafes: all to make it easier and more pleasant. Also, are you flexible in letting your employees work remotely? Our systems administrator Elliott is about to embark on a long-term cross-country journey working from an RV.

Environment: Open office, cubicle, cafe, work-from-home? Loud/quiet? Social or focused? 24×7 or work-life balance? Different environments appeal to different people.

Team: Who will they be working with? A company with 100,000 people might have 100 brilliant ones you’d want to work with, but ultimately we work with our core team. Who will your prospective hires be working with?

Market: Some people are passionate about gaming, others biotech, still others food. The market you’re targeting will get different people excited.

Product: Have an amazing product people love? Highlight that. If you’re lucky, your potential hire is already a fan.

Mission: Curing cancer, making people happy, and other company missions inspire people to strive to be part of the journey. Our mission is to make storing data astonishingly easy and low-cost. If you care about data, information, knowledge, and progress, our mission helps drive all of them.

Culture: I left this for last, but believe it’s the most important. What is the culture of your company? Finding people who want to work in the culture of your organization is critical. If they like the culture, they’ll fit and continue it. We’ve worked hard to build a culture that’s collaborative, friendly, supportive, and open; one in which people like coming to work. For example, the five founders started with (and still have) the same compensation and equity. That started a culture of “we’re all in this together.” Build a culture that will attract the people you want, and convey what the culture is.

Writing The Job Description

Most job descriptions focus on the all the requirements the candidate must meet. While important to communicate, the job description should first sell the job. Why would the appropriate candidate want the job? Then share some of the requirements you think are critical. Remember that people read not just what you say but how you say it. Try to write in a way that conveys what it is like to actually be at the company. Ahin, our VP of Marketing, said the job description itself was one of the things that attracted him to the company.

Orchestrating Interviews

Much can be said about interviewing well. I’m just going to say this: make sure that everyone who is interviewing knows that their job is not only to evaluate the candidate, but give them a sense of the culture, and sell them on the company. At Backblaze, we often have one person interview core prospects solely for company/culture fit.

Onboarding

Hiring success shouldn’t be defined by finding and hiring the right person, but instead by the right person being successful and happy within the organization. Ensure someone (usually their manager) provides them guidance on what they should be concentrating on doing during their first day, first week, and thereafter. Giving new employees opportunities and guidance so that they can achieve early wins and feel socially integrated into the company does wonders for bringing people on board smoothly

In Closing

Our Director of Production Systems, Chris, said to me the other day that he looks for companies where he can work on “interesting problems with nice people.” I’m hoping you’ll find your own version of that and find this post useful in looking for your early and critical hires.

Of course, I’d be remiss if I didn’t say, if you know of anyone looking for a place with “interesting problems with nice people,” Backblaze is hiring. 😉

The post Early Challenges: Making Critical Hires appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Security Breaches Don’t Affect Stock Price

2018-01-19 Bruce Schneier

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/01/security_breach.html

Interesting research: “Long-term market implications of data breaches, not,” by Russell Lange and Eric W. Burger.

Abstract: This report assesses the impact disclosure of data breaches has on the total returns and volatility of the affected companies’ stock, with a focus on the results relative to the performance of the firms’ peer industries, as represented through selected indices rather than the market as a whole. Financial performance is considered over a range of dates from 3 days post-breach through 6 months post-breach, in order to provide a longer-term perspective on the impact of the breach announcement.
Key findings:
While the difference in stock price between the sampled breached companies and their peers was negative (1.13%) in the first 3 days following announcement of a breach, by the 14th day the return difference had rebounded to + 0.05%, and on average remained positive through the period assessed.
For the differences in the breached companies’ betas and the beta of their peer sets, the differences in the means of 8 months pre-breach versus post-breach was not meaningful at 90, 180, and 360 day post-breach periods.
For the differences in the breached companies’ beta correlations against the peer indices pre- and post-breach, the difference in the means of the rolling 60 day correlation 8 months pre- breach versus post-breach was not meaningful at 90, 180, and 360 day post-breach periods.
In regression analysis, use of the number of accessed records, date, data sensitivity, and malicious versus accidental leak as variables failed to yield an R2 greater than 16.15% for response variables of 3, 14, 60, and 90 day return differential, excess beta differential, and rolling beta correlation differential, indicating that the financial impact on breached companies was highly idiosyncratic.
Based on returns, the most impacted industries at the 3 day post-breach date were U.S. Financial Services, Transportation, and Global Telecom. At the 90 day post-breach date, the three most impacted industries were U.S. Financial Services, U.S. Healthcare, and Global Telecom.

The market isn’t going to fix this. If we want better security, we need to regulate the market.

Note: The article is behind a paywall. An older version is here. A similar article is here.

Wanted: Sales Engineer

2018-01-10 Yev

Post Syndicated from Yev original https://www.backblaze.com/blog/wanted-sales-engineer/

At inception, Backblaze was a consumer company. Thousands upon thousands of individuals came to our website and gave us $5/mo to keep their data safe. But, we didn’t sell business solutions. It took us years before we had a sales team. In the last couple of years, we’ve released products that businesses of all sizes love: Backblaze B2 Cloud Storage and Backblaze for Business Computer Backup. Those businesses want to integrate Backblaze deeply into their infrastructure, so it’s time to hire our first Sales Engineer!

Some Backblaze Perks:

Competitive healthcare plans
Competitive compensation and 401k
All employees receive Option grants
Unlimited vacation days
Strong coffee
Fully stocked Micro kitchen
Catered breakfast and lunches
Awesome people who work on awesome projects
Childcare bonus
Normal work hours
Get to bring your pets into the office
San Mateo Office – located near Caltrain and Highways 101 & 280.

Backblaze B2 cloud storage is a building block for almost any computing service that requires storage. Customers need our help integrating B2 into iOS apps to Docker containers. Some customers integrate directly to the API using the programming language of their choice, others want to solve a specific problem using ready made software, already integrated with B2.

At the same time, our computer backup product is deepening it’s integration into enterprise IT systems. We are commonly asked for how to set Windows policies, integrate with Active Directory, and install the client via remote management tools.

We are looking for a sales engineer who can help our customers navigate the integration of Backblaze into their technical environments.

Are you 1/2” deep into many different technologies, and unafraid to dive deeper?

Can you confidently talk with customers about their technology, even if you have to look up all the acronyms right after the call?

Are you excited to setup complicated software in a lab and write knowledge base articles about your work?

Then Backblaze is the place for you!

Enough about Backblaze already, what’s in it for me?
In this role, you will be given the opportunity to learn about the technologies that drive innovation today; diverse technologies that customers are using day in and out. And more importantly, you’ll learn how to learn new technologies.

Just as an example, in the past 12 months, we’ve had the opportunity to learn and become experts in these diverse technologies:

How to setup VM servers for lab environments, both on-prem and using cloud services.
Create an automatically “resetting” demo environment for the sales team.
Setup Microsoft Domain Controllers with Active Directory and AD Federation Services.
Learn the basics of OAUTH and web single sign on (SSO).
Archive video workflows from camera to media asset management systems.
How upload/download files from Javascript by enabling CORS.
How to install and monitor online backup installations using RMM tools, like JAMF.
Tape (LTO) systems. (Yes – people still use tape for storage!)

How can I know if I’ll succeed in this role?

You have:

Confidence. Be able to ask customers questions about their environments and convey to them your technical acumen.
Curiosity. Always want to learn about customers’ situations, how they got there and what problems they are trying to solve.
Organization. You’ll work with customers, integration partners, and Backblaze team members on projects of various lengths. You can context switch and either have a great memory or keep copious notes. Your checklists have their own checklists.

You are versed in:

The fundamentals of Windows, Linux and Mac OS X operating systems. You shouldn’t be afraid to use a command line.

Building, installing, integrating and configuring applications on any operating system.

Debugging failures – reading logs, monitoring usage, effective google searching to fix problems excites you.

The basics of TCP/IP networking and the HTTP protocol.

Novice development skills in any programming/scripting language. Have basic understanding of data structures and program flow.

Your background contains:

Bachelor’s degree in computer science or the equivalent.

2+ years of experience as a pre or post-sales engineer.

The right extra credit:
There are literally hundreds of previous experiences you can have had that would make you perfect for this job. Some experiences that we know would be helpful for us are below, but make sure you tell us your stories!

Experience using or programming against Amazon S3.

Experience with large on-prem storage – NAS, SAN, Object. And backing up data on such storage with tools like Veeam, Veritas and others.

Experience with photo or video media. Media archiving is a key market for Backblaze B2.

Program arduinos to automatically feed your dog.

Experience programming against web or REST APIs. (Point us towards your projects, if they are open source and available to link to.)

Experience with sales tools like Salesforce.

3D print door stops.

Experience with Windows Servers, Active Directory, Group policies and the like.

What’s it like working with the Sales team?
The Backblaze sales team collaborates. We help each other out by sharing ideas, templates, and our customer’s experiences. When we talk about our accomplishments, there is no “I did this,” only “we”. We are truly a team.

If this all sounds like you:

Send an email to [email protected] with the position in the subject line.
Tell us a bit about your Sales Engineering experience.
Include your resume.

The post Wanted: Sales Engineer appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

re:Invent Recap – Announcements to Boost Enterprise Innovation with Windows

2017-12-07 Jeff Barr

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/reinvent-recap-announcements-to-boost-enterprise-innovation-with-windows/

My colleague Sandy Carter delivered the Enterprise Innovation State of the Union last week at AWS re:Invent. She wrote the guest post below to recap the announcements that she made from the stage.

— Jeff;

“I want my company to innovate, but I am not convinced we can execute successfully.” Far too many times I have heard this fear expressed by senior executives that I have met at different points in my career. In fact, a recent study published by Price Waterhouse Coopers found that while 93% of executives depend on innovation to drive growth, more than half are challenged to take innovative ideas to market quickly in a scalable way.

Many customers are struggling with how to drive enterprise innovation, so I was thrilled to share the stage at AWS re:Invent this past week with several senior executives who have successfully broken this mold to drive amazing enterprise innovation. In particular, I want to thank Parag Karnik from Johnson & Johnson, Bill Rothe from Hess Corporation, Dave Williams from Just Eat, and Olga Lagunova from Pitney Bowes for sharing their stories of innovation, creativity, and solid execution.

Among the many new announcements from AWS this past week, I am particularly excited about the following newly-launched AWS products and programs that I announced at re:Invent to drive new innovations by our enterprise customers:

AI: New Deep Learning Amazon Machine Image (AMI) on EC2 Windows
As I shared at re:Invent, customers such as Infor are already successfully leveraging artificial intelligence tools on AWS to deliver tailored, industry-specific applications to their customers. We want to facilitate more of our Windows developers to get started quickly and easily with AI, leveraging machine learning based tools with popular deep learning frameworks, such as Apache MXNet, TensorFlow, and Caffe2. In order to enable this, I announced at re:Invent that AWS now offers a new Deep Learning AMI for Microsoft Windows. The AMI is tailored to facilitate large scale training of deep-learning models, and enables quick and easy setup of Windows Server-based compute resources for machine learning applications.

IoT: Visualize and Analyze SQL and IoT Data
Forecasts show as many as 31 billion IoT devices by 2020. AWS wants every Windows customer to take advantage of the data available from their devices. Pitney Bowes, for example, now has more than 130,000 IoT devices streaming data to AWS. Using machine learning, Pitney Bowes enriches and analyzes data to enhance their customer experience, improve efficiencies, and create new data products. AWS IoT Analytics can now be leveraged to run analytics on IoT data and get insights that help you make better and more accurate decisions for IoT applications and machine learning use cases. AWS IoT Analytics can automatically enrich IoT device data with contextual metadata such as your SQL Server transactional data.

New Capabilities for .NET Developers on AWS
In addition to all of the enhancements we’ve introduced to deliver a first class experience to Windows developers on AWS, we announced that we are including .NET Core 2.0 support in AWS Lambda and AWS CodeBuild, which will be available for broader use early next year. .NET Core 2.0 packs a number of new features such as Razor pages, better compatibility with .NET framework, more than double the number of APIs compared to the previous versions, and much more. With this announcement, you will be able to take advantage of all latest .NET Core features on Lambda and CodeBuild for building modern serverless and DevOps centric solutions.

Simplified Backup for Windows Applications
We recently introduced application consistent snapshots with Microsoft Volume Shadow Copy Service (VSS). This enables you to take VSS snapshots with Amazon Elastic Block Store (EBS) for your running Windows instances without the need to create custom scripts or to shut down the instances. This removes the overhead associated with backing up your Windows applications.

License optimization for BYOL
AWS provides you a wide variety of instance types and families that best meet your workload needs. If you are using software licensed by the number of vCPUs, you want the ability to further tweak vCPU count to optimize license spend. I announced the upcoming ability to optimize CPUs for EC2, giving you greater control over your EC2 instances on two fronts:

You can specify a custom number of vCPUs when launching new instances to save on vCPU based licensing costs. For example, SQL Server licensing spend.
You can disable Hyper-Threading Technology for workloads that perform well with single-threaded CPUs, like some high-performance computing (HPC) applications.

Using these capabilities, customers who bring their own license (BYOL) will be able to optimize their license usage and save on the license costs.

Server Migration Service for Hyper-V Virtual Machines
As Bill Rothe from Hess Corporation shared at re:Invent, Hess has successfully migrated a wide range of workloads to the cloud, including SQL Server, SharePoint, SAP HANA, and many others. AWS Server Migration Service (SMS) now supports Hyper-V virtual machine (VM) migration, in order to further support enterprise migrations like these. AWS Server Migration Service will enable you to more easily co-ordinate large-scale server migrations from on-premise Hyper-V environments to AWS. AWS Server Migration Service allows you to automate, schedule, and track incremental replications of live server volumes. The replicated volumes are encrypted in transit and saved as a new Amazon Machine Image (AMI), which can be launched as an EC2 instance on AWS.

Microsoft Premier Support for AWS End-Customers
I was pleased to announce that Microsoft and AWS have developed new areas of support integration to help ensure a great customer experience. Microsoft Premier Support is on board to help AWS assist end customers. AWS Support engineers can escalate directly to Microsoft Support on behalf of AWS customers running Microsoft workloads.

Best Practice Tools: HIPAA Compliance and Digital Innovation Workshop
In November, we updated our HIPAA-focused white paper, outlining how you can use AWS to create HIPAA-compliant applications. In the first quarter of next year, we will publish a HIPAA Implementation Guide that expands on our HIPAA Quick Start to enable you to follow strict security, compliance, and risk management controls for common healthcare use cases. I was also pleased to award a Digital Innovation Workshop to one of our customers in my re:Invent session, and look forward to seeing more customers take advantage of this workshop.

AWS: The Continuous Innovation Cloud
A common thread we see across customers is that continuous innovation from AWS enables their ongoing reinvention. Continuous innovation means that you are always getting a newer, better offering every single day. Sometimes it is in the form of brand new services and capabilities, and sometimes it is happening invisibly, under the covers where your environment just keeps getting better. I invite you to learn more about how you can accelerate your innovation journey with recently launched AWS services and AWS best practices. If you are migrating Windows workloads, speak with your AWS sales representative or an AWS Microsoft Workloads Competency Partner to learn how you can leverage our re:Think for Windows program for credits to start your migration.

– Sandy Carter, Vice President, AWS

AWS Big Data & Analytics Sessions at Re:Invent 2017

2017-11-24 Roy Ben-Alta

Post Syndicated from Roy Ben-Alta original https://aws.amazon.com/blogs/big-data/aws-big-data-analytics-sessions-at-reinvent-2017/

We can’t believe that there are just few days left before re:Invent 2017. If you are attending this year, you’ll want to check out our Big Data sessions! The Big Data and Machine Learning categories are bigger than ever. As in previous years, you can find these sessions in various tracks, including Analytics & Big Data, Deep Learning Summit, Artificial Intelligence & Machine Learning, Architecture, and Databases.

We have great sessions from organizations and companies like Vanguard, Cox Automotive, Pinterest, Netflix, FINRA, Amtrak, AmazonFresh, Sysco Foods, Twilio, American Heart Association, Expedia, Esri, Nextdoor, and many more. All sessions are recorded and made available on YouTube. In addition, all slide decks from the sessions will be available on SlideShare.net after the conference.

This post highlights the sessions that will be presented as part of the Analytics & Big Data track, as well as relevant sessions from other tracks like Architecture, Artificial Intelligence & Machine Learning, and IoT. If you’re interested in Machine Learning sessions, don’t forget to check out our Guide to Machine Learning at re:Invent 2017.

This year’s session catalog contains the following breakout sessions.

DAT201 – AWS Database and Analytics State of the Union – Thursday

Raju Gulabani, VP, Database, Analytics and AI at AWS will discuss the evolution of database and analytics services in AWS, the new database and analytics services and features we launched this year, and our vision for continued innovation in this space. We are witnessing an unprecedented growth in the amount of data collected, in many different forms. Storage, management, and analysis of this data require database services that scale and perform in ways not possible before. AWS offers a collection of database and other data services—including Amazon Aurora, Amazon DynamoDB, Amazon RDS, Amazon Redshift, Amazon ElastiCache, Amazon Kinesis, and Amazon EMR—to process, store, manage, and analyze data. In this session, we provide an overview of AWS database and analytics services and discuss how customers are using these services today.

Deep dive customer use cases

ABD401 – How Netflix Monitors Applications in Near Real-Time with Amazon Kinesis
Thousands of services work in concert to deliver millions of hours of video streams to Netflix customers every day. These applications vary in size, function, and technology, but they all make use of the Netflix network to communicate. Understanding the interactions between these services is a daunting challenge both because of the sheer volume of traffic and the dynamic nature of deployments. In this session, we first discuss why Netflix chose Kinesis Streams to address these challenges at scale. We then dive deep into how Netflix uses Kinesis Streams to enrich network traffic logs and identify usage patterns in real time. Lastly, we cover how Netflix uses this system to build comprehensive dependency maps, increase network efficiency, and improve failure resiliency. From this session, you will learn how to build a real-time application monitoring system using network traffic logs and get real-time, actionable insights.

SRV319 – How Nextdoor Built a Scalable, Serverless Data Pipeline for Billions of Events per Day

In this session, learn how Nextdoor replaced their home-grown data pipeline based on a topology of Flume nodes with a completely serverless architecture based on Kinesis and Lambda. By making these changes, they improved both the reliability of their data and the delivery times of billions of records of data to their Amazon S3–based data lake and Amazon Redshift cluster. Nextdoor is a private social networking service for neighborhoods.

ABD205 – Taking a Page Out of Ivy Tech’s Book: Using Data for Student Success
Data speaks. Discover how Ivy Tech, the nation’s largest singly accredited community college, uses AWS to gather, analyze, and take action on student behavioral data for the betterment of over 3,100 students. This session outlines the process from inception to implementation across the state of Indiana and highlights how Ivy Tech’s model can be applied to your own complex business problems.

ABD207 – Leveraging AWS to Fight Financial Crime and Protect National Security
Banks aren’t known to share data and collaborate with one another. But that is exactly what the Mid-Sized Bank Coalition of America (MBCA) is doing to fight digital financial crime—and protect national security. Using the AWS Cloud, the MBCA developed a shared data analytics utility that processes terabytes of non-competitive customer account, transaction, and government risk data. The intelligence produced from the data helps banks increase the efficiency of their operations, cut labor and operating costs, and reduce false positive volumes. The collective intelligence also allows greater enforcement of Anti-Money Laundering (AML) regulations by helping members detect internal risks—and identify the challenges to detecting these risks in the first place. This session demonstrates how the AWS Cloud supports the MBCA to deliver advanced data analytics, provide consistent operating models across financial institutions, reduce costs, and strengthen national security.

ABD208 – Cox Automotive Empowered to Scale with Splunk Cloud & AWS and Explores New Innovation with Amazon Kinesis Firehose
In this session, learn how Cox Automotive is using Splunk Cloud for real time visibility into its AWS and hybrid environments to achieve near instantaneous MTTI, reduce auction incidents by 90%, and proactively predict outages. We also introduce a highly anticipated capability that allows you to ingest, transform, and analyze data in real time using Splunk and Amazon Kinesis Firehose to gain valuable insights from your cloud resources. It’s now quicker and easier than ever to gain access to analytics-driven infrastructure monitoring using Splunk Enterprise & Splunk Cloud.

ABD209 – Accelerating the Speed of Innovation with a Data Sciences Data & Analytics Hub at Takeda
Historically, silos of data, analytics, and processes across functions, stages of development, and geography created a barrier to R&D efficiency. Gathering the right data necessary for decision-making was challenging due to issues of accessibility, trust, and timeliness. In this session, learn how Takeda is undergoing a transformation in R&D to increase the speed-to-market of high-impact therapies to improve patient lives. The Data and Analytics Hub was built, with Deloitte, to address these issues and support the efficient generation of data insights for functions such as clinical operations, clinical development, medical affairs, portfolio management, and R&D finance. In the AWS hosted data lake, this data is processed, integrated, and made available to business end users through data visualization interfaces, and to data scientists through direct connectivity. Learn how Takeda has achieved significant time reductions—from weeks to minutes—to gather and provision data that has the potential to reduce cycle times in drug development. The hub also enables more efficient operations and alignment to achieve product goals through cross functional team accountability and collaboration due to the ability to access the same cross domain data.

ABD210 – Modernizing Amtrak: Serverless Solution for Real-Time Data Capabilities
As the nation’s only high-speed intercity passenger rail provider, Amtrak needs to know critical information to run their business such as: Who’s onboard any train at any time? How are booking and revenue trending? Amtrak was faced with unpredictable and often slow response times from existing databases, ranging from seconds to hours; existing booking and revenue dashboards were spreadsheet-based and manual; multiple copies of data were stored in different repositories, lacking integration and consistency; and operations and maintenance (O&M) costs were relatively high. Join us as we demonstrate how Deloitte and Amtrak successfully went live with a cloud-native operational database and analytical datamart for near-real-time reporting in under six months. We highlight the specific challenges and the modernization of architecture on an AWS native Platform as a Service (PaaS) solution. The solution includes cloud-native components such as AWS Lambda for microservices, Amazon Kinesis and AWS Data Pipeline for moving data, Amazon S3 for storage, Amazon DynamoDB for a managed NoSQL database service, and Amazon Redshift for near-real time reports and dashboards. Deloitte’s solution enabled “at scale” processing of 1 million transactions/day and up to 2K transactions/minute. It provided flexibility and scalability, largely eliminate the need for system management, and dramatically reduce operating costs. Moreover, it laid the groundwork for decommissioning legacy systems, anticipated to save at least $1M over 3 years.

ABD211 – Sysco Foods: A Journey from Too Much Data to Curated Insights
In this session, we detail Sysco’s journey from a company focused on hindsight-based reporting to one focused on insights and foresight. For this shift, Sysco moved from multiple data warehouses to an AWS ecosystem, including Amazon Redshift, Amazon EMR, AWS Data Pipeline, and more. As the team at Sysco worked with Tableau, they gained agile insight across their business. Learn how Sysco decided to use AWS, how they scaled, and how they became more strategic with the AWS ecosystem and Tableau.

ABD217 – From Batch to Streaming: How Amazon Flex Uses Real-time Analytics to Deliver Packages on Time
Reducing the time to get actionable insights from data is important to all businesses, and customers who employ batch data analytics tools are exploring the benefits of streaming analytics. Learn best practices to extend your architecture from data warehouses and databases to real-time solutions. Learn how to use Amazon Kinesis to get real-time data insights and integrate them with Amazon Aurora, Amazon RDS, Amazon Redshift, and Amazon S3. The Amazon Flex team describes how they used streaming analytics in their Amazon Flex mobile app, used by Amazon delivery drivers to deliver millions of packages each month on time. They discuss the architecture that enabled the move from a batch processing system to a real-time system, overcoming the challenges of migrating existing batch data to streaming data, and how to benefit from real-time analytics.

ABD218 – How EuroLeague Basketball Uses IoT Analytics to Engage Fans
IoT and big data have made their way out of industrial applications, general automation, and consumer goods, and are now a valuable tool for improving consumer engagement across a number of industries, including media, entertainment, and sports. The low cost and ease of implementation of AWS analytics services and AWS IoT have allowed AGT, a leader in IoT, to develop their IoTA analytics platform. Using IoTA, AGT brought a tailored solution to EuroLeague Basketball for real-time content production and fan engagement during the 2017-18 season. In this session, we take a deep dive into how this solution is architected for secure, scalable, and highly performant data collection from athletes, coaches, and fans. We also talk about how the data is transformed into insights and integrated into a content generation pipeline. Lastly, we demonstrate how this solution can be easily adapted for other industries and applications.

ABD222 – How to Confidently Unleash Data to Meet the Needs of Your Entire Organization
Where are you on the spectrum of IT leaders? Are you confident that you’re providing the technology and solutions that consistently meet or exceed the needs of your internal customers? Do your peers at the executive table see you as an innovative technology leader? Innovative IT leaders understand the value of getting data and analytics directly into the hands of decision makers, and into their own. In this session, Daren Thayne, Domo’s Chief Technology Officer, shares how innovative IT leaders are helping drive a culture change at their organizations. See how transformative it can be to have real-time access to all of the data that’ is relevant to YOUR job (including a complete view of your entire AWS environment), as well as understand how it can help you lead the way in applying that same pattern throughout your entire company

ABD303 – Developing an Insights Platform – Sysco’s Journey from Disparate Systems to Data Lake and Beyond
Sysco has nearly 200 operating companies across its multiple lines of business throughout the United States, Canada, Central/South America, and Europe. As the global leader in food services, Sysco identified the need to streamline the collection, transformation, and presentation of data produced by the distributed units and systems, into a central data ecosystem. Sysco’s Business Intelligence and Analytics team addressed these requirements by creating a data lake with scalable analytics and query engines leveraging AWS services. In this session, Sysco will outline their journey from a hindsight reporting focused company to an insights driven organization. They will cover solution architecture, challenges, and lessons learned from deploying a self-service insights platform. They will also walk through the design patterns they used and how they designed the solution to provide predictive analytics using Amazon Redshift Spectrum, Amazon S3, Amazon EMR, AWS Glue, Amazon Elasticsearch Service and other AWS services.

ABD309 – How Twilio Scaled Its Data-Driven Culture
As a leading cloud communications platform, Twilio has always been strongly data-driven. But as headcount and data volumes grew—and grew quickly—they faced many new challenges. One-off, static reports work when you’re a small startup, but how do you support a growth stage company to a successful IPO and beyond? Today, Twilio’s data team relies on AWS and Looker to provide data access to 700 colleagues. Departments have the data they need to make decisions, and cloud-based scale means they get answers fast. Data delivers real-business value at Twilio, providing a 360-degree view of their customer, product, and business. In this session, you hear firsthand stories directly from the Twilio data team and learn real-world tips for fostering a truly data-driven culture at scale.

ABD310 – How FINRA Secures Its Big Data and Data Science Platform on AWS
FINRA uses big data and data science technologies to detect fraud, market manipulation, and insider trading across US capital markets. As a financial regulator, FINRA analyzes highly sensitive data, so information security is critical. Learn how FINRA secures its Amazon S3 Data Lake and its data science platform on Amazon EMR and Amazon Redshift, while empowering data scientists with tools they need to be effective. In addition, FINRA shares AWS security best practices, covering topics such as AMI updates, micro segmentation, encryption, key management, logging, identity and access management, and compliance.

ABD331 – Log Analytics at Expedia Using Amazon Elasticsearch Service
Expedia uses Amazon Elasticsearch Service (Amazon ES) for a variety of mission-critical use cases, ranging from log aggregation to application monitoring and pricing optimization. In this session, the Expedia team reviews how they use Amazon ES and Kibana to analyze and visualize Docker startup logs, AWS CloudTrail data, and application metrics. They share best practices for architecting a scalable, secure log analytics solution using Amazon ES, so you can add new data sources almost effortlessly and get insights quickly

ABD316 – American Heart Association: Finding Cures to Heart Disease Through the Power of Technology
Combining disparate datasets and making them accessible to data scientists and researchers is a prevalent challenge for many organizations, not just in healthcare research. American Heart Association (AHA) has built a data science platform using Amazon EMR, Amazon Elasticsearch Service, and other AWS services, that corrals multiple datasets and enables advanced research on phenotype and genotype datasets, aimed at curing heart diseases. In this session, we present how AHA built this platform and the key challenges they addressed with the solution. We also provide a demo of the platform, and leave you with suggestions and next steps so you can build similar solutions for your use cases

ABD319 – Tooling Up for Efficiency: DIY Solutions @ Netflix
At Netflix, we have traditionally approached cloud efficiency from a human standpoint, whether it be in-person meetings with the largest service teams or manually flipping reservations. Over time, we realized that these manual processes are not scalable as the business continues to grow. Therefore, in the past year, we have focused on building out tools that allow us to make more insightful, data-driven decisions around capacity and efficiency. In this session, we discuss the DIY applications, dashboards, and processes we built to help with capacity and efficiency. We start at the ten thousand foot view to understand the unique business and cloud problems that drove us to create these products, and discuss implementation details, including the challenges encountered along the way. Tools discussed include Picsou, the successor to our AWS billing file cost analyzer; Libra, an easy-to-use reservation conversion application; and cost and efficiency dashboards that relay useful financial context to 50+ engineering teams and managers.

ABD312 – Deep Dive: Migrating Big Data Workloads to AWS
Customers are migrating their analytics, data processing (ETL), and data science workloads running on Apache Hadoop, Spark, and data warehouse appliances from on-premise deployments to AWS in order to save costs, increase availability, and improve performance. AWS offers a broad set of analytics services, including solutions for batch processing, stream processing, machine learning, data workflow orchestration, and data warehousing. This session will focus on identifying the components and workflows in your current environment; and providing the best practices to migrate these workloads to the right AWS data analytics product. We will cover services such as Amazon EMR, Amazon Athena, Amazon Redshift, Amazon Kinesis, and more. We will also feature Vanguard, an American investment management company based in Malvern, Pennsylvania with over $4.4 trillion in assets under management. Ritesh Shah, Sr. Program Manager for Cloud Analytics Program at Vanguard, will describe how they orchestrated their migration to AWS analytics services, including Hadoop and Spark workloads to Amazon EMR. Ritesh will highlight the technical challenges they faced and overcame along the way, as well as share common recommendations and tuning tips to accelerate the time to production.

ABD402 – How Esri Optimizes Massive Image Archives for Analytics in the Cloud
Petabyte scale archives of satellites, planes, and drones imagery continue to grow exponentially. They mostly exist as semi-structured data, but they are only valuable when accessed and processed by a wide range of products for both visualization and analysis. This session provides an overview of how ArcGIS indexes and structures data so that any part of it can be quickly accessed, processed, and analyzed by reading only the minimum amount of data needed for the task. In this session, we share best practices for structuring and compressing massive datasets in Amazon S3, so it can be analyzed efficiently. We also review a number of different image formats, including GeoTIFF (used for the Public Datasets on AWS program, Landsat on AWS), cloud optimized GeoTIFF, MRF, and CRF as well as different compression approaches to show the effect on processing performance. Finally, we provide examples of how this technology has been used to help image processing and analysis for the response to Hurricane Harvey.

ABD329 – A Look Under the Hood – How Amazon.com Uses AWS Services for Analytics at Massive Scale
Amazon’s consumer business continues to grow, and so does the volume of data and the number and complexity of the analytics done in support of the business. In this session, we talk about how Amazon.com uses AWS technologies to build a scalable environment for data and analytics. We look at how Amazon is evolving the world of data warehousing with a combination of a data lake and parallel, scalable compute engines such as Amazon EMR and Amazon Redshift.

ABD327 – Migrating Your Traditional Data Warehouse to a Modern Data Lake
In this session, we discuss the latest features of Amazon Redshift and Redshift Spectrum, and take a deep dive into its architecture and inner workings. We share many of the recent availability, performance, and management enhancements and how they improve your end user experience. You also hear from 21st Century Fox, who presents a case study of their fast migration from an on-premises data warehouse to Amazon Redshift. Learn how they are expanding their data warehouse to a data lake that encompasses multiple data sources and data formats. This architecture helps them tie together siloed business units and get actionable 360-degree insights across their consumer base.

MCL202 – Ally Bank & Cognizant: Transforming Customer Experience Using Amazon Alexa
Given the increasing popularity of natural language interfaces such as Voice as User technology or conversational artificial intelligence (AI), Ally® Bank was looking to interact with customers by enabling direct transactions through conversation or voice. They also needed to develop a capability that allows third parties to connect to the bank securely for information sharing and exchange, using oAuth, an authentication protocol seen as the future of secure banking technology. Cognizant’s Architecture team partnered with Ally Bank’s Enterprise Architecture group and identified the right product for oAuth integration with Amazon Alexa and third-party technologies. In this session, we discuss how building products with conversational AI helps Ally Bank offer an innovative customer experience; increase retention through improved data-driven personalization; increase the efficiency and convenience of customer service; and gain deep insights into customer needs through data analysis and predictive analytics to offer new products and services.

MCL317 – Orchestrating Machine Learning Training for Netflix Recommendations
At Netflix, we use machine learning (ML) algorithms extensively to recommend relevant titles to our 100+ million members based on their tastes. Everything on the member home page is an evidence-driven, A/B-tested experience that we roll out backed by ML models. These models are trained using Meson, our workflow orchestration system. Meson distinguishes itself from other workflow engines by handling more sophisticated execution graphs, such as loops and parameterized fan-outs. Meson can schedule Spark jobs, Docker containers, bash scripts, gists of Scala code, and more. Meson also provides a rich visual interface for monitoring active workflows and inspecting execution logs. It has a powerful Scala DSL for authoring workflows as well as the REST API. In this session, we focus on how Meson trains recommendation ML models in production, and how we have re-architected it to scale up for a growing need of broad ETL applications within Netflix. As a driver for this change, we have had to evolve the persistence layer for Meson. We talk about how we migrated from Cassandra to Amazon RDS backed by Amazon Aurora

MCL350 – Humans vs. the Machines: How Pinterest Uses Amazon Mechanical Turk’s Worker Community to Improve Machine Learning
Ever since the term “crowdsourcing” was coined in 2006, it’s been a buzzword for technology companies and social institutions. In the technology sector, crowdsourcing is instrumental for verifying machine learning algorithms, which, in turn, improves the user’s experience. In this session, we explore how Pinterest adapted to an increased reliability on human evaluation to improve their product, with a focus on how they’ve integrated with Mechanical Turk’s platform. This presentation is aimed at engineers, analysts, program managers, and product managers who are interested in how companies rely on Mechanical Turk’s human evaluation platform to better understand content and improve machine learning algorithms. The discussion focuses on the analysis and product decisions related to building a high quality crowdsourcing system that takes advantage of Mechanical Turk’s powerful worker community.

Service sessions: architecture and best practices

Featuring Amazon EMR, Amazon Athena, Amazon Kinesis, AWS Glue, Amazon QuickSight, Amazon Redshift, Amazon Elasticsearch Service, and Amazon DynamoDB.

ABD201 – Big Data Architectural Patterns and Best Practices on AWS
In this session, we simplify big data processing as a data bus comprising various stages: collect, store, process, analyze, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost

ABD202 – Best Practices for Building Serverless Big Data Applications
Serverless technologies let you build and scale applications and services rapidly without the need to provision or manage servers. In this session, we show you how to incorporate serverless concepts into your big data architectures. We explore the concepts behind and benefits of serverless architectures for big data, looking at design patterns to ingest, store, process, and visualize your data. Along the way, we explain when and how you can use serverless technologies to streamline data processing, minimize infrastructure management, and improve agility and robustness and share a reference architecture using a combination of cloud and open source technologies to solve your big data problems. Topics include: use cases and best practices for serverless big data applications; leveraging AWS technologies such as Amazon DynamoDB, Amazon S3, Amazon Kinesis, AWS Lambda, Amazon Athena, and Amazon EMR; and serverless ETL, event processing, ad hoc analysis, and real-time analytics.

ABD206 – Building Visualizations and Dashboards with Amazon QuickSight
Just as a picture is worth a thousand words, a visual is worth a thousand data points. A key aspect of our ability to gain insights from our data is to look for patterns, and these patterns are often not evident when we simply look at data in tables. The right visualization will help you gain a deeper understanding in a much quicker timeframe. In this session, we will show you how to quickly and easily visualize your data using Amazon QuickSight. We will show you how you can connect to data sources, generate custom metrics and calculations, create comprehensive business dashboards with various chart types, and setup filters and drill downs to slice and dice the data.

ABD203 – Real-Time Streaming Applications on AWS: Use Cases and Patterns
To win in the marketplace and provide differentiated customer experiences, businesses need to be able to use live data in real time to facilitate fast decision making. In this session, you learn common streaming data processing use cases and architectures. First, we give an overview of streaming data and AWS streaming data capabilities. Next, we look at a few customer examples and their real-time streaming applications. Finally, we walk through common architectures and design patterns of top streaming data use cases.

ABD213 – How to Build a Data Lake with AWS Glue Data Catalog
As data volumes grow and customers store more data on AWS, they often have valuable data that is not easily discoverable and available for analytics. The AWS Glue Data Catalog provides a central view of your data lake, making data readily available for analytics. We introduce key features of the AWS Glue Data Catalog and its use cases. Learn how crawlers can automatically discover your data, extract relevant metadata, and add it as table definitions to the AWS Glue Data Catalog. We will also explore the integration between AWS Glue Data Catalog and Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.

ABD214 – Real-time User Insights for Mobile and Web Applications with Amazon Pinpoint
With customers demanding relevant and real-time experiences across a range of devices, digital businesses are looking to gather user data at scale, understand this data, and respond to customer needs instantly. This requires tools that can record large volumes of user data in a structured fashion, and then instantly make this data available to generate insights. In this session, we demonstrate how you can use Amazon Pinpoint to capture user data in a structured yet flexible manner. Further, we demonstrate how this data can be set up for instant consumption using services like Amazon Kinesis Firehose and Amazon Redshift. We walk through example data based on real world scenarios, to illustrate how Amazon Pinpoint lets you easily organize millions of events, record them in real-time, and store them for further analysis.

ABD223 – IT Innovators: New Technology for Leveraging Data to Enable Agility, Innovation, and Business Optimization
Companies of all sizes are looking for technology to efficiently leverage data and their existing IT investments to stay competitive and understand where to find new growth. Regardless of where companies are in their data-driven journey, they face greater demands for information by customers, prospects, partners, vendors and employees. All stakeholders inside and outside the organization want information on-demand or in “real time”, available anywhere on any device. They want to use it to optimize business outcomes without having to rely on complex software tools or human gatekeepers to relevant information. Learn how IT innovators at companies such as MasterCard, Jefferson Health, and TELUS are using Domo’s Business Cloud to help their organizations more effectively leverage data at scale.

ABD301 – Analyzing Streaming Data in Real Time with Amazon Kinesis
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. In this session, we present an end-to-end streaming data solution using Kinesis Streams for data ingestion, Kinesis Analytics for real-time processing, and Kinesis Firehose for persistence. We review in detail how to write SQL queries using streaming data and discuss best practices to optimize and monitor your Kinesis Analytics applications. Lastly, we discuss how to estimate the cost of the entire system

ABD302 – Real-Time Data Exploration and Analytics with Amazon Elasticsearch Service and Kibana
In this session, we use Apache web logs as example and show you how to build an end-to-end analytics solution. First, we cover how to configure an Amazon ES cluster and ingest data using Amazon Kinesis Firehose. We look at best practices for choosing instance types, storage options, shard counts, and index rotations based on the throughput of incoming data. Then we demonstrate how to set up a Kibana dashboard and build custom dashboard widgets. Finally, we review approaches for generating custom, ad-hoc reports.

ABD304 – Best Practices for Data Warehousing with Amazon Redshift & Redshift Spectrum
Most companies are over-run with data, yet they lack critical insights to make timely and accurate business decisions. They are missing the opportunity to combine large amounts of new, unstructured big data that resides outside their data warehouse with trusted, structured data inside their data warehouse. In this session, we take an in-depth look at how modern data warehousing blends and analyzes all your data, inside and outside your data warehouse without moving the data, to give you deeper insights to run your business. We will cover best practices on how to design optimal schemas, load data efficiently, and optimize your queries to deliver high throughput and performance.

ABD305 – Design Patterns and Best Practices for Data Analytics with Amazon EMR
Amazon EMR is one of the largest Hadoop operators in the world, enabling customers to run ETL, machine learning, real-time processing, data science, and low-latency SQL at petabyte scale. In this session, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters, and other Amazon EMR architectural best practices. We talk about lowering cost with Auto Scaling and Spot Instances, and security best practices for encryption and fine-grained access control. Finally, we dive into some of our recent launches to keep you current on our latest features.

ABD307 – Deep Analytics for Global AWS Marketing Organization
To meet the needs of the global marketing organization, the AWS marketing analytics team built a scalable platform that allows the data science team to deliver custom econometric and machine learning models for end user self-service. To meet data security standards, we use end-to-end data encryption and different AWS services such as Amazon Redshift, Amazon RDS, Amazon S3, Amazon EMR with Apache Spark and Auto Scaling. In this session, you see real examples of how we have scaled and automated critical analysis, such as calculating the impact of marketing programs like re:Invent and prioritizing leads for our sales teams.

ABD311 – Deploying Business Analytics at Enterprise Scale with Amazon QuickSight
One of the biggest tradeoffs customers usually make when deploying BI solutions at scale is agility versus governance. Large-scale BI implementations with the right governance structure can take months to design and deploy. In this session, learn how you can avoid making this tradeoff using Amazon QuickSight. Learn how to easily deploy Amazon QuickSight to thousands of users using Active Directory and Federated SSO, while securely accessing your data sources in Amazon VPCs or on-premises. We also cover how to control access to your datasets, implement row-level security, create scheduled email reports, and audit access to your data.

ABD315 – Building Serverless ETL Pipelines with AWS Glue
Organizations need to gain insight and knowledge from a growing number of Internet of Things (IoT), APIs, clickstreams, unstructured and log data sources. However, organizations are also often limited by legacy data warehouses and ETL processes that were designed for transactional data. In this session, we introduce key ETL features of AWS Glue, cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL flows for your data lake. We discuss how to build scalable, efficient, and serverless ETL pipelines using AWS Glue. Additionally, Merck will share how they built an end-to-end ETL pipeline for their application release management system, and launched it in production in less than a week using AWS Glue.

ABD318 – Architecting a data lake with Amazon S3, Amazon Kinesis, and Amazon Athena
Learn how to architect a data lake where different teams within your organization can publish and consume data in a self-service manner. As organizations aim to become more data-driven, data engineering teams have to build architectures that can cater to the needs of diverse users – from developers, to business analysts, to data scientists. Each of these user groups employs different tools, have different data needs and access data in different ways. In this talk, we will dive deep into assembling a data lake using Amazon S3, Amazon Kinesis, Amazon Athena, Amazon EMR, and AWS Glue. The session will feature Mohit Rao, Architect and Integration lead at Atlassian, the maker of products such as JIRA, Confluence, and Stride. First, we will look at a couple of common architectures for building a data lake. Then we will show how Atlassian built a self-service data lake, where any team within the company can publish a dataset to be consumed by a broad set of users.

ABD 323 Extending Analytics Beyond the Data warehouse

Companies have valuable data that they may not be analyzing due to the complexity, scalability, and performance issues of loading the data into their data warehouse. However, with the right tools, you can extend your analytics to query data in your data lake—with no loading required. Amazon Redshift Spectrum extends the analytic power of Amazon Redshift beyond data stored in your data warehouse to run SQL queries directly against vast amounts of unstructured data in your Amazon S3 data lake. This gives you the freedom to store your data where you want, in the format you want, and have it available for analytics when you need it. Join a discussion with AWS solution architects to ask question.

ABD330 – Combining Batch and Stream Processing to Get the Best of Both Worlds
Today, many architects and developers are looking to build solutions that integrate batch and real-time data processing, and deliver the best of both approaches. Lambda architecture (not to be confused with the AWS Lambda service) is a design pattern that leverages both batch and real-time processing within a single solution to meet the latency, accuracy, and throughput requirements of big data use cases. Come join us for a discussion on how to implement Lambda architecture (batch, speed, and serving layers) and best practices for data processing, loading, and performance tuning

ABD335 – Real-Time Anomaly Detection Using Amazon Kinesis
Amazon Kinesis Analytics offers a built-in machine learning algorithm that you can use to easily detect anomalies in your VPC network traffic and improve security monitoring. Join us for an interactive discussion on how to stream your VPC flow Logs to Amazon Kinesis Streams and identify anomalies using Kinesis Analytics.

ABD339 – Deep Dive and Best Practices for Amazon Athena
Amazon Athena is an interactive query service that enables you to process data directly from Amazon S3 without the need for infrastructure. Since its launch at re:invent 2016, several organizations have adopted Athena as the central tool to process all their data. In this talk, we dive deep into the most common use cases, including working with other AWS services. We review the best practices for creating tables and partitions and performance optimizations. We also dive into how Athena handles security, authorization, and authentication. Lastly, we hear from a customer who has reduced costs and improved time to market by deploying Athena across their organization.

We look forward to meeting you at re:Invent 2017!

About the Author

Roy Ben-Alta is a solution architect and principal business development manager at Amazon Web Services in New York. He focuses on Data Analytics and ML Technologies, working with AWS customers to build innovative data-driven products.

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Tag Archives: Healthcare

Building Simpler Genomics Workflows on AWS Step Functions

Using a job status poller

With Step Functions Service Integrations

Genomics workflow: Before and after

Conclusion

Security Breaches Don’t Affect Stock Price

The collective thoughts of the interwebz

Using a job status poller

With Step Functions Service Integrations

Genomics workflow: Before and after

Conclusion

Which data sources does Amazon QuickSight support?

Answers in instants

Typical Amazon QuickSight workflow

Visualizations created in Amazon QuickSight with sample datasets

Summary

Additional Reading

The data tsunami is upon us

Reference Architecture

Ingestion

Specific Industry Use Cases

Storage

Security

Conclusion

How Ransomware Attacks Typically Work

What’s Changed With the Latest Ransomware Attacks?

Timeline of SamSam History and Exploits

March 2016SamSam appears

April 2016SamSam finds new targets

April 2017New tactics include RDP

January 2018Municipalities attacked

February 2018Attack volume increases

March 2018SamSam shuts down Atlanta

How to Defend Against SamSam and Other Ransomware Attacks

Please Tell Us About Your Experiences with Ransomware

VPC Endpoints for SNS in action

Set up VPC Endpoints for SNS

Help ensure your security and compliance

Summary

Organizational structure in Organizations

How Service Control Policy evaluation logic works

An example structure with nested OUs and SCPs

Conclusion

OHDSI application architecture on AWS

Deploying OHDSI on AWS

Cost of deploying this environment

Summary

Additional Reading

About the Author

What You’ll Be Doing

What You Should Have

Highly Desirable Skills

Some Backblaze Perks

If this sounds like you — follow these steps:

Who Should Be Your First Hires

When To Hire

Where To Find the Right People

Helping People To Find You

Convincing People To Join

In Closing

Deep dive customer use cases

Service sessions: architecture and best practices

About the Author

The collective thoughts of the interwebz

March 2016
SamSam appears

April 2016
SamSam finds new targets

April 2017
New tactics include RDP

January 2018
Municipalities attacked

February 2018
Attack volume increases

March 2018
SamSam shuts down Atlanta