Tag Archives: AWS Partner Network

Analyze Apache Parquet optimized data using Amazon Kinesis Data Firehose, Amazon Athena, and Amazon Redshift

Post Syndicated from Roy Hasson original https://aws.amazon.com/blogs/big-data/analyzing-apache-parquet-optimized-data-using-amazon-kinesis-data-firehose-amazon-athena-and-amazon-redshift/

Amazon Kinesis Data Firehose is the easiest way to capture and stream data into a data lake built on Amazon S3. This data can be anything—from AWS service logs like AWS CloudTrail log files, Amazon VPC Flow Logs, Application Load Balancer logs, and others. It can also be IoT events, game events, and much more. To efficiently query this data, a time-consuming ETL (extract, transform, and load) process is required to massage and convert the data to an optimal file format, which increases the time to insight. This situation is less than ideal, especially for real-time data that loses its value over time.

To solve this common challenge, Kinesis Data Firehose can now save data to Amazon S3 in Apache Parquet or Apache ORC format. These are optimized columnar formats that are highly recommended for best performance and cost-savings when querying data in S3. This feature directly benefits you if you use Amazon Athena, Amazon Redshift, AWS Glue, Amazon EMR, or any other big data tools that are available from the AWS Partner Network and through the open-source community.

Amazon Connect is a simple-to-use, cloud-based contact center service that makes it easy for any business to provide a great customer experience at a lower cost than common alternatives. Its open platform design enables easy integration with other systems. One of those systems is Amazon Kinesis—in particular, Kinesis Data Streams and Kinesis Data Firehose.

What’s really exciting is that you can now save events from Amazon Connect to S3 in Apache Parquet format. You can then perform analytics using Amazon Athena and Amazon Redshift Spectrum in real time, taking advantage of this key performance and cost optimization. Of course, Amazon Connect is only one example. This new capability opens the door for a great deal of opportunity, especially as organizations continue to build their data lakes.

Amazon Connect includes an array of analytics views in the Administrator dashboard. But you might want to run other types of analysis. In this post, I describe how to set up a data stream from Amazon Connect through Kinesis Data Streams and Kinesis Data Firehose and out to S3, and then perform analytics using Athena and Amazon Redshift Spectrum. I focus primarily on the Kinesis Data Firehose support for Parquet and its integration with the AWS Glue Data Catalog, Amazon Athena, and Amazon Redshift.

Solution overview

Here is how the solution is laid out:

 

 

The following sections walk you through each of these steps to set up the pipeline.

1. Define the schema

When Kinesis Data Firehose processes incoming events and converts the data to Parquet, it needs to know which schema to apply. The reason is that many times, incoming events contain all or some of the expected fields based on which values the producers are advertising. A typical process is to normalize the schema during a batch ETL job so that you end up with a consistent schema that can easily be understood and queried. Doing this introduces latency due to the nature of the batch process. To overcome this issue, Kinesis Data Firehose requires the schema to be defined in advance.

To see the available columns and structures, see Amazon Connect Agent Event Streams. For the purpose of simplicity, I opted to make all the columns of type String rather than create the nested structures. But you can definitely do that if you want.

The simplest way to define the schema is to create a table in the Amazon Athena console. Open the Athena console, and paste the following create table statement, substituting your own S3 bucket and prefix for where your event data will be stored. A Data Catalog database is a logical container that holds the different tables that you can create. The default database name shown here should already exist. If it doesn’t, you can create it or use another database that you’ve already created.

CREATE EXTERNAL TABLE default.kfhconnectblog (
  awsaccountid string,
  agentarn string,
  currentagentsnapshot string,
  eventid string,
  eventtimestamp string,
  eventtype string,
  instancearn string,
  previousagentsnapshot string,
  version string
)
STORED AS parquet
LOCATION 's3://your_bucket/kfhconnectblog/'
TBLPROPERTIES ("parquet.compression"="SNAPPY")

That’s all you have to do to prepare the schema for Kinesis Data Firehose.

2. Define the data streams

Next, you need to define the Kinesis data streams that will be used to stream the Amazon Connect events.  Open the Kinesis Data Streams console and create two streams.  You can configure them with only one shard each because you don’t have a lot of data right now.

3. Define the Kinesis Data Firehose delivery stream for Parquet

Let’s configure the Data Firehose delivery stream using the data stream as the source and Amazon S3 as the output. Start by opening the Kinesis Data Firehose console and creating a new data delivery stream. Give it a name, and associate it with the Kinesis data stream that you created in Step 2.

As shown in the following screenshot, enable Record format conversion (1) and choose Apache Parquet (2). As you can see, Apache ORC is also supported. Scroll down and provide the AWS Glue Data Catalog database name (3) and table names (4) that you created in Step 1. Choose Next.

To make things easier, the output S3 bucket and prefix fields are automatically populated using the values that you defined in the LOCATION parameter of the create table statement from Step 1. Pretty cool. Additionally, you have the option to save the raw events into another location as defined in the Source record S3 backup section. Don’t forget to add a trailing forward slash “ / “ so that Data Firehose creates the date partitions inside that prefix.

On the next page, in the S3 buffer conditions section, there is a note about configuring a large buffer size. The Parquet file format is highly efficient in how it stores and compresses data. Increasing the buffer size allows you to pack more rows into each output file, which is preferred and gives you the most benefit from Parquet.

Compression using Snappy is automatically enabled for both Parquet and ORC. You can modify the compression algorithm by using the Kinesis Data Firehose API and update the OutputFormatConfiguration.

Be sure to also enable Amazon CloudWatch Logs so that you can debug any issues that you might run into.

Lastly, finalize the creation of the Firehose delivery stream, and continue on to the next section.

4. Set up the Amazon Connect contact center

After setting up the Kinesis pipeline, you now need to set up a simple contact center in Amazon Connect. The Getting Started page provides clear instructions on how to set up your environment, acquire a phone number, and create an agent to accept calls.

After setting up the contact center, in the Amazon Connect console, choose your Instance Alias, and then choose Data Streaming. Under Agent Event, choose the Kinesis data stream that you created in Step 2, and then choose Save.

At this point, your pipeline is complete.  Agent events from Amazon Connect are generated as agents go about their day. Events are sent via Kinesis Data Streams to Kinesis Data Firehose, which converts the event data from JSON to Parquet and stores it in S3. Athena and Amazon Redshift Spectrum can simply query the data without any additional work.

So let’s generate some data. Go back into the Administrator console for your Amazon Connect contact center, and create an agent to handle incoming calls. In this example, I creatively named mine Agent One. After it is created, Agent One can get to work and log into their console and set their availability to Available so that they are ready to receive calls.

To make the data a bit more interesting, I also created a second agent, Agent Two. I then made some incoming and outgoing calls and caused some failures to occur, so I now have enough data available to analyze.

5. Analyze the data with Athena

Let’s open the Athena console and run some queries. One thing you’ll notice is that when we created the schema for the dataset, we defined some of the fields as Strings even though in the documentation they were complex structures.  The reason for doing that was simply to show some of the flexibility of Athena to be able to parse JSON data. However, you can define nested structures in your table schema so that Kinesis Data Firehose applies the appropriate schema to the Parquet file.

Let’s run the first query to see which agents have logged into the system.

The query might look complex, but it’s fairly straightforward:

WITH dataset AS (
  SELECT 
    from_iso8601_timestamp(eventtimestamp) AS event_ts,
    eventtype,
    -- CURRENT STATE
    json_extract_scalar(
      currentagentsnapshot,
      '$.agentstatus.name') AS current_status,
    from_iso8601_timestamp(
      json_extract_scalar(
        currentagentsnapshot,
        '$.agentstatus.starttimestamp')) AS current_starttimestamp,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.firstname') AS current_firstname,
    json_extract_scalar(
      currentagentsnapshot,
      '$.configuration.lastname') AS current_lastname,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.username') AS current_username,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.routingprofile.defaultoutboundqueue.name') AS               current_outboundqueue,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.routingprofile.inboundqueues[0].name') as current_inboundqueue,
    -- PREVIOUS STATE
    json_extract_scalar(
      previousagentsnapshot, 
      '$.agentstatus.name') as prev_status,
    from_iso8601_timestamp(
      json_extract_scalar(
        previousagentsnapshot, 
       '$.agentstatus.starttimestamp')) as prev_starttimestamp,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.firstname') as prev_firstname,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.lastname') as prev_lastname,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.username') as prev_username,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.routingprofile.defaultoutboundqueue.name') as current_outboundqueue,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.routingprofile.inboundqueues[0].name') as prev_inboundqueue
  from kfhconnectblog
  where eventtype <> 'HEART_BEAT'
)
SELECT
  current_status as status,
  current_username as username,
  event_ts
FROM dataset
WHERE eventtype = 'LOGIN' AND current_username <> ''
ORDER BY event_ts DESC

The query output looks something like this:

Here is another query that shows the sessions each of the agents engaged with. It tells us where they were incoming or outgoing, if they were completed, and where there were missed or failed calls.

WITH src AS (
  SELECT
     eventid,
     json_extract_scalar(currentagentsnapshot, '$.configuration.username') as username,
     cast(json_extract(currentagentsnapshot, '$.contacts') AS ARRAY(JSON)) as c,
     cast(json_extract(previousagentsnapshot, '$.contacts') AS ARRAY(JSON)) as p
  from kfhconnectblog
),
src2 AS (
  SELECT *
  FROM src CROSS JOIN UNNEST (c, p) AS contacts(c_item, p_item)
),
dataset AS (
SELECT 
  eventid,
  username,
  json_extract_scalar(c_item, '$.contactid') as c_contactid,
  json_extract_scalar(c_item, '$.channel') as c_channel,
  json_extract_scalar(c_item, '$.initiationmethod') as c_direction,
  json_extract_scalar(c_item, '$.queue.name') as c_queue,
  json_extract_scalar(c_item, '$.state') as c_state,
  from_iso8601_timestamp(json_extract_scalar(c_item, '$.statestarttimestamp')) as c_ts,
  
  json_extract_scalar(p_item, '$.contactid') as p_contactid,
  json_extract_scalar(p_item, '$.channel') as p_channel,
  json_extract_scalar(p_item, '$.initiationmethod') as p_direction,
  json_extract_scalar(p_item, '$.queue.name') as p_queue,
  json_extract_scalar(p_item, '$.state') as p_state,
  from_iso8601_timestamp(json_extract_scalar(p_item, '$.statestarttimestamp')) as p_ts
FROM src2
)
SELECT 
  username,
  c_channel as channel,
  c_direction as direction,
  p_state as prev_state,
  c_state as current_state,
  c_ts as current_ts,
  c_contactid as id
FROM dataset
WHERE c_contactid = p_contactid
ORDER BY id DESC, current_ts ASC

The query output looks similar to the following:

6. Analyze the data with Amazon Redshift Spectrum

With Amazon Redshift Spectrum, you can query data directly in S3 using your existing Amazon Redshift data warehouse cluster. Because the data is already in Parquet format, Redshift Spectrum gets the same great benefits that Athena does.

Here is a simple query to show querying the same data from Amazon Redshift. Note that to do this, you need to first create an external schema in Amazon Redshift that points to the AWS Glue Data Catalog.

SELECT 
  eventtype,
  json_extract_path_text(currentagentsnapshot,'agentstatus','name') AS current_status,
  json_extract_path_text(currentagentsnapshot, 'configuration','firstname') AS current_firstname,
  json_extract_path_text(currentagentsnapshot, 'configuration','lastname') AS current_lastname,
  json_extract_path_text(
    currentagentsnapshot,
    'configuration','routingprofile','defaultoutboundqueue','name') AS current_outboundqueue,
FROM default_schema.kfhconnectblog

The following shows the query output:

Summary

In this post, I showed you how to use Kinesis Data Firehose to ingest and convert data to columnar file format, enabling real-time analysis using Athena and Amazon Redshift. This great feature enables a level of optimization in both cost and performance that you need when storing and analyzing large amounts of data. This feature is equally important if you are investing in building data lakes on AWS.

 


Additional Reading

If you found this post useful, be sure to check out Analyzing VPC Flow Logs with Amazon Kinesis Firehose, Amazon Athena, and Amazon QuickSight and Work with partitioned data in AWS Glue.


About the Author

Roy Hasson is a Global Business Development Manager for AWS Analytics. He works with customers around the globe to design solutions to meet their data processing, analytics and business intelligence needs. Roy is big Manchester United fan cheering his team on and hanging out with his family.

 

 

 

Introducing the AWS Machine Learning Competency for Consulting Partners

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/introducing-the-aws-machine-learning-competency-for-consulting-partners/

Today I’m excited to announce a new Machine Learning Competency for Consulting Partners in the Amazon Partner Network (APN). This AWS Competency program allows APN Consulting Partners to demonstrate a deep expertise in machine learning on AWS by providing solutions that enable machine learning and data science workflows for their customers. This new AWS Competency is in addition to the Machine Learning comptency for our APN Technology Partners, that we launched at the re:Invent 2017 partner summit.

These APN Consulting Partners help organizations solve their machine learning and data challenges through:

  • Providing data services that help data scientists and machine learning practitioners prepare their enterprise data for training.
  • Platform solutions that provide data scientists and machine learning practitioners with tools to take their data, train models, and make predictions on new data.
  • SaaS and API solutions to enable predictive capabilities within customer applications.

Why work with an AWS Machine Learning Competency Partner?

The AWS Competency Program helps customers find the most qualified partners with deep expertise. AWS Machine Learning Competency Partners undergo a strict validation of their capabilities to demonstrate technical proficiency and proven customer success with AWS machine learning tools.

If you’re an AWS customer interested in machine learning workloads on AWS, check out our AWS Machine Learning launch partners below:

 

Interested in becoming an AWS Machine Learning Competency Partner?

APN Partners with experience in Machine Learning can learn more about becoming an AWS Machine Learning Competency Partner here. To learn more about the benefits of joining the AWS Partner Network, see our APN Partner website.

Thanks to the AWS Partner Team for their help with this post!
Randall

Amazon Redshift – 2017 Recap

Post Syndicated from Larry Heathcote original https://aws.amazon.com/blogs/big-data/amazon-redshift-2017-recap/

We have been busy adding new features and capabilities to Amazon Redshift, and we wanted to give you a glimpse of what we’ve been doing over the past year. In this article, we recap a few of our enhancements and provide a set of resources that you can use to learn more and get the most out of your Amazon Redshift implementation.

In 2017, we made more than 30 announcements about Amazon Redshift. We listened to you, our customers, and delivered Redshift Spectrum, a feature of Amazon Redshift, that gives you the ability to extend analytics to your data lake—without moving data. We launched new DC2 nodes, doubling performance at the same price. We also announced many new features that provide greater scalability, better performance, more automation, and easier ways to manage your analytics workloads.

To see a full list of our launches, visit our what’s new page—and be sure to subscribe to our RSS feed.

Major launches in 2017

Amazon Redshift Spectrumextend analytics to your data lake, without moving data

We launched Amazon Redshift Spectrum to give you the freedom to store data in Amazon S3, in open file formats, and have it available for analytics without the need to load it into your Amazon Redshift cluster. It enables you to easily join datasets across Redshift clusters and S3 to provide unique insights that you would not be able to obtain by querying independent data silos.

With Redshift Spectrum, you can run SQL queries against data in an Amazon S3 data lake as easily as you analyze data stored in Amazon Redshift. And you can do it without loading data or resizing the Amazon Redshift cluster based on growing data volumes. Redshift Spectrum separates compute and storage to meet workload demands for data size, concurrency, and performance. Redshift Spectrum scales processing across thousands of nodes, so results are fast, even with massive datasets and complex queries. You can query open file formats that you already use—such as Apache Avro, CSV, Grok, ORC, Apache Parquet, RCFile, RegexSerDe, SequenceFile, TextFile, and TSV—directly in Amazon S3, without any data movement.

For complex queries, Redshift Spectrum provided a 67 percent performance gain,” said Rafi Ton, CEO, NUVIAD. “Using the Parquet data format, Redshift Spectrum delivered an 80 percent performance improvement. For us, this was substantial.

To learn more about Redshift Spectrum, watch our AWS Summit session Intro to Amazon Redshift Spectrum: Now Query Exabytes of Data in S3, and read our announcement blog post Amazon Redshift Spectrum – Exabyte-Scale In-Place Queries of S3 Data.

DC2 nodes—twice the performance of DC1 at the same price

We launched second-generation Dense Compute (DC2) nodes to provide low latency and high throughput for demanding data warehousing workloads. DC2 nodes feature powerful Intel E5-2686 v4 (Broadwell) CPUs, fast DDR4 memory, and NVMe-based solid state disks (SSDs). We’ve tuned Amazon Redshift to take advantage of the better CPU, network, and disk on DC2 nodes, providing up to twice the performance of DC1 at the same price. Our DC2.8xlarge instances now provide twice the memory per slice of data and an optimized storage layout with 30 percent better storage utilization.

Redshift allows us to quickly spin up clusters and provide our data scientists with a fast and easy method to access data and generate insights,” said Bradley Todd, technology architect at Liberty Mutual. “We saw a 9x reduction in month-end reporting time with Redshift DC2 nodes as compared to DC1.”

Read our customer testimonials to see the performance gains our customers are experiencing with DC2 nodes. To learn more, read our blog post Amazon Redshift Dense Compute (DC2) Nodes Deliver Twice the Performance as DC1 at the Same Price.

Performance enhancements— 3x-5x faster queries

On average, our customers are seeing 3x to 5x performance gains for most of their critical workloads.

We introduced short query acceleration to speed up execution of queries such as reports, dashboards, and interactive analysis. Short query acceleration uses machine learning to predict the execution time of a query, and to move short running queries to an express short query queue for faster processing.

We launched results caching to deliver sub-second response times for queries that are repeated, such as dashboards, visualizations, and those from BI tools. Results caching has an added benefit of freeing up resources to improve the performance of all other queries.

We also introduced late materialization to reduce the amount of data scanned for queries with predicate filters by batching and factoring in the filtering of predicates before fetching data blocks in the next column. For example, if only 10 percent of the table rows satisfy the predicate filters, Amazon Redshift can potentially save 90 percent of the I/O for the remaining columns to improve query performance.

We launched query monitoring rules and pre-defined rule templates. These features make it easier for you to set metrics-based performance boundaries for workload management (WLM) queries, and specify what action to take when a query goes beyond those boundaries. For example, for a queue that’s dedicated to short-running queries, you might create a rule that aborts queries that run for more than 60 seconds. To track poorly designed queries, you might have another rule that logs queries that contain nested loops.

Customer insights

Amazon Redshift and Redshift Spectrum serve customers across a variety of industries and sizes, from startups to large enterprises. Visit our customer page to see the success that customers are having with our recent enhancements. Learn how companies like Liberty Mutual Insurance saw a 9x reduction in month-end reporting time using DC2 nodes. On this page, you can find case studies, videos, and other content that show how our customers are using Amazon Redshift to drive innovation and business results.

In addition, check out these resources to learn about the success our customers are having building out a data warehouse and data lake integration solution with Amazon Redshift:

Partner solutions

You can enhance your Amazon Redshift data warehouse by working with industry-leading experts. Our AWS Partner Network (APN) Partners have certified their solutions to work with Amazon Redshift. They offer software, tools, integration, and consulting services to help you at every step. Visit our Amazon Redshift Partner page and choose an APN Partner. Or, use AWS Marketplace to find and immediately start using third-party software.

To see what our Partners are saying about Amazon Redshift Spectrum and our DC2 nodes mentioned earlier, read these blog posts:

Resources

Blog posts

Visit the AWS Big Data Blog for a list of all Amazon Redshift articles.

YouTube videos

GitHub

Our community of experts contribute on GitHub to provide tips and hints that can help you get the most out of your deployment. Visit GitHub frequently to get the latest technical guidance, code samples, administrative task automation utilities, the analyze & vacuum schema utility, and more.

Customer support

If you are evaluating or considering a proof of concept with Amazon Redshift, or you need assistance migrating your on-premises or other cloud-based data warehouse to Amazon Redshift, our team of product experts and solutions architects can help you with architecting, sizing, and optimizing your data warehouse. Contact us using this support request form, and let us know how we can assist you.

If you are an Amazon Redshift customer, we offer a no-cost health check program. Our team of database engineers and solutions architects give you recommendations for optimizing Amazon Redshift and Amazon Redshift Spectrum for your specific workloads. To learn more, email us at [email protected].

If you have any questions, email us at [email protected].

 


Additional Reading

If you found this post useful, be sure to check out Amazon Redshift Spectrum – Exabyte-Scale In-Place Queries of S3 Data, Using Amazon Redshift for Fast Analytical Reports and How to Migrate Your Oracle Data Warehouse to Amazon Redshift Using AWS SCT and AWS DMS.


About the Author

Larry Heathcote is a Principle Product Marketing Manager at Amazon Web Services for data warehousing and analytics. Larry is passionate about seeing the results of data-driven insights on business outcomes. He enjoys family time, home projects, grilling out and the taste of classic barbeque.

 

 

 

Natural Language Processing at Clemson University – 1.1 Million vCPUs & EC2 Spot Instances

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/natural-language-processing-at-clemson-university-1-1-million-vcpus-ec2-spot-instances/

My colleague Sanjay Padhi shared the guest post below in order to recognize an important milestone in the use of EC2 Spot Instances.

Jeff;


A group of researchers from Clemson University achieved a remarkable milestone while studying topic modeling, an important component of machine learning associated with natural language processing, breaking the record for creating the largest high-performance cluster by using more than 1,100,000 vCPUs on Amazon EC2 Spot Instances running in a single AWS region. The researchers conducted nearly half a million topic modeling experiments to study how human language is processed by computers. Topic modeling helps in discovering the underlying themes that are present across a collection of documents. Topic models are important because they are used to forecast business trends and help in making policy or funding decisions. These topic models can be run with many different parameters and the goal of the experiments is to explore how these parameters affect the model outputs.

The Experiment
Professor Amy Apon, Co-Director of the Complex Systems, Analytics and Visualization Institute at Clemson University with Professor Alexander Herzog and graduate students Brandon Posey and Christopher Gropp in collaboration with members of the AWS team as well as AWS Partner Omnibond performed the experiments.  They used software infrastructure based on CloudyCluster that provisions high performance computing clusters on dynamically allocated AWS resources using Amazon EC2 Spot Fleet. Spot Fleet is a collection of biddable spot instances in EC2 responsible for maintaining a target capacity specified during the request. The SLURM scheduler was used as an overlay virtual workload manager for the data analytics workflows. The team developed additional provisioning and workflow automation software as shown below for the design and orchestration of the experiments. This setup allowed them to evaluate various topic models on different data sets with massively parallel parameter sweeps on dynamically allocated AWS resources. This framework can easily be used beyond the current study for other scientific applications that use parallel computing.

Ramping to 1.1 Million vCPUs
The figure below shows elastic, automatic expansion of resources as a function of time, in the US East (Northern Virginia) Region. At just after 21:40 (GMT-1) on Aug. 26, 2017, the number of vCPUs utilized was 1,119,196. Clemson researchers also took advantage of the new per-second billing for the EC2 instances that they launched. The vCPU count usage is comparable to the core count on the largest supercomputers in the world.

Here’s the breakdown of the EC2 instance types that they used:

Campus resources at Clemson funded by the National Science Foundation were used to determine an effective configuration for the AWS experiments as compared to campus resources, and the AWS cloud resources complement the campus resources for large-scale experiments.

Meet the Team
Here’s the team that ran the experiment (Professor Alexander Herzog, graduate students Christopher Gropp and Brandon Posey, and Professor Amy Apon):

Professor Apon said about the experiment:

I am absolutely thrilled with the outcome of this experiment. The graduate students on the project are amazing. They used resources from AWS and Omnibond and developed a new software infrastructure to perform research at a scale and time-to-completion not possible with only campus resources. Per-second billing was a key enabler of these experiments.

Boyd Wilson (CEO, Omnibond, member of the AWS Partner Network) told me:

Participating in this project was exciting, seeing how the Clemson team developed a provisioning and workflow automation tool that tied into CloudyCluster to build a huge Spot Fleet supercomputer in a single region in AWS was outstanding.

About the Experiment
The experiments test parameter combinations on a range of topics and other parameters used in the topic model. The topic model outputs are stored in Amazon S3 and are currently being analyzed. The models have been applied to 17 years of computer science journal abstracts (533,560 documents and 32,551,540 words) and full text papers from the NIPS (Neural Information Processing Systems) Conference (2,484 documents and 3,280,697 words). This study allows the research team to systematically measure and analyze the impact of parameters and model selection on model convergence, topic composition and quality.

Looking Forward
This study constitutes an interaction between computer science, artificial intelligence, and high performance computing. Papers describing the full study are being submitted for peer-reviewed publication. I hope that you enjoyed this brief insight into the ways in which AWS is helping to break the boundaries in the frontiers of natural language processing!

Sanjay Padhi, Ph.D, AWS Research and Technical Computing

 

Skill up on how to perform CI/CD with AWS Developer tools

Post Syndicated from Chirag Dhull original https://aws.amazon.com/blogs/devops/skill-up-on-how-to-perform-cicd-with-aws-devops-tools/

This is a guest post from Paul Duvall, CTO of Stelligent, a division of HOSTING.

I co-founded Stelligent, a technology services company that provides DevOps Automation on AWS as a result of my own frustration in implementing all the “behind the scenes” infrastructure (including builds, tests, deployments, etc.) on software projects on which I was developing software. At Stelligent, we have worked with numerous customers looking to get software delivered to users quicker and with greater confidence. This sounds simple but it often consists of properly configuring and integrating myriad tools including, but not limited to, version control, build, static analysis, testing, security, deployment, and software release orchestration. What some might not realize is that there’s a new breed of build, deploy, test, and release tools that help reduce much of the undifferentiated heavy lifting of deploying and releasing software to users.

 
I’ve been using AWS since 2009 and I, along with many at Stelligent – have worked with the AWS Service Teams as part of the AWS Developer Tools betas that are now generally available (including AWS CodePipeline, AWS CodeCommit, AWS CodeBuild, and AWS CodeDeploy). I’ve combined the experience we’ve had with customers along with this specialized knowledge of the AWS Developer and Management Tools to provide a unique course that shows multiple ways to use these services to deliver software to users quicker and with confidence.

 
In DevOps Essentials on AWS, you’ll learn how to accelerate software delivery and speed up feedback loops by learning how to use AWS Developer Tools to automate infrastructure and deployment pipelines for applications running on AWS. The course demonstrates solutions for various DevOps use cases for Amazon EC2, AWS OpsWorks, AWS Elastic Beanstalk, AWS Lambda (Serverless), Amazon ECS (Containers), while defining infrastructure as code and learning more about AWS Developer Tools including AWS CodeStar, AWS CodeCommit, AWS CodeBuild, AWS CodePipeline, and AWS CodeDeploy.

 
In this course, you see me use the AWS Developer and Management Tools to create comprehensive continuous delivery solutions for a sample application using many types of AWS service platforms. You can run the exact same sample and/or fork the GitHub repository (https://github.com/stelligent/devops-essentials) and extend or modify the solutions. I’m excited to share how you can use AWS Developer Tools to create these solutions for your customers as well. There’s also an accompanying website for the course (http://www.devopsessentialsaws.com/) that I use in the video to walk through the course examples which link to resources located in GitHub or Amazon S3. In this course, you will learn how to:

  • Use AWS Developer and Management Tools to create a full-lifecycle software delivery solution
  • Use AWS CloudFormation to automate the provisioning of all AWS resources
  • Use AWS CodePipeline to orchestrate the deployments of all applications
  • Use AWS CodeCommit while deploying an application onto EC2 instances using AWS CodeBuild and AWS CodeDeploy
  • Deploy applications using AWS OpsWorks and AWS Elastic Beanstalk
  • Deploy an application using Amazon EC2 Container Service (ECS) along with AWS CloudFormation
  • Deploy serverless applications that use AWS Lambda and API Gateway
  • Integrate all AWS Developer Tools into an end-to-end solution with AWS CodeStar

To learn more, see DevOps Essentials on AWS video course on Udemy. For a limited time, you can enroll in this course for $40 and save 80%, a $160 saving. Simply use the code AWSDEV17.

 
Stelligent, an AWS Partner Network Advanced Consulting Partner holds the AWS DevOps Competency and over 100 AWS technical certifications. To stay updated on DevOps best practices, visit www.stelligent.com.

AWS Partner Webinar Series – August 2017

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/aws-partner-webinar-series-august-2017/

We love bringing our customers helpful information and we have another cool series we are excited to tell you about. The AWS Partner Webinar Series is a selection of live and recorded presentations covering a broad range of topics at varying technical levels and scale. A little different from our AWS Online TechTalks, each AWS Partner Webinar is hosted by an AWS solutions architect and an AWS Competency Partner who has successfully helped customers evaluate and implement the tools, techniques, and technologies of AWS.

Check out this month’s webinars and let us know which ones you found the most helpful! All schedule times are shown in the Pacific Time (PDT) time zone.

Security Webinars

Sophos
Seeing More Clearly: ATLO Software Secures Online Training Solutions for Correctional Facilities with SophosUTM on AWS Link.
August 17th, 2017 | 10:00 AM PDT

F5
F5 on AWS: How MailControl Improved their Application Visibility and Security
August 23, 2017 | 10:00 AM PDT

Big Data Webinars

Tableau, Matillion, 47Lining, NorthBay
Unlock Insights and Reduce Costs by Modernizing Your Data Warehouse on AWS
August 22, 2017 | 10:00 AM PDT

Storage Webinars

StorReduce
How Globe Telecom does Primary Backups via StorReduce to the AWS Cloud
August 29, 2017 | 8:00 AM PDT

Commvault
Moving Forward Faster: How Monash University Automated Data Movement for 3500 Virtual Machines to AWS with Commvault
August 29, 2017 | 1:00 PM PDT

Dell EMC
Moving Forward Faster: Protect Your Workloads on AWS With Increased Scale and Performance
August 30, 2017 | 11:00 AM PDT

Druva
How Hatco Protects Against Ransomware with Druva on AWS
September 13, 2017 | 10:00 AM PDT

AWS HIPAA Eligibility Update (July 2017) – Eight Additional Services

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-hipaa-eligibility-update-july-2017-eight-additional-services/

It is time for an update on our on-going effort to make AWS a great host for healthcare and life sciences applications. As you can see from our Health Customer Stories page, Philips, VergeHealth, and Cambia (to choose a few) trust AWS with Protected Health Information (PHI) and Personally Identifying Information (PII) as part of their efforts to comply with HIPAA and HITECH.

In May we announced that we added Amazon API Gateway, AWS Direct Connect, AWS Database Migration Service, and Amazon Simple Queue Service (SQS) to our list of HIPAA eligible services and discussed our how customers and partners are putting them to use.

Eight More Eligible Services
Today I am happy to share the news that we are adding another eight services to the list:

Amazon CloudFront can now be utilized to enhance the delivery and transfer of Protected Health Information data to applications on the Internet. By providing a completely secure and encryptable pathway, CloudFront can now be used as a part of applications that need to cache PHI. This includes applications for viewing lab results or imaging data, and those that transfer PHI from Healthcare Information Exchanges (HIEs).

AWS WAF can now be used to protect applications running on AWS which operate on PHI such as patient care portals, patient scheduling systems, and HIEs. Requests and responses containing encrypted PHI and PII can now pass through AWS WAF.

AWS Shield can now be used to protect web applications such as patient care portals and scheduling systems that operate on encrypted PHI from DDoS attacks.

Amazon S3 Transfer Acceleration can now be used to accelerate the bulk transfer of large amounts of research, genetics, informatics, insurance, or payer/payment data containing PHI/PII information. Transfers can take place between a pair of AWS Regions or from an on-premises system and an AWS Region.

Amazon WorkSpaces can now be used by researchers, informaticists, hospital administrators and other users to analyze, visualize or process PHI/PII data using on-demand Windows virtual desktops.

AWS Directory Service can now be used to connect the authentication and authorization systems of organizations that use or process PHI/PII to their resources in the AWS Cloud. For example, healthcare providers operating hybrid cloud environments can now use AWS Directory Services to allow their users to easily transition between cloud and on-premises resources.

Amazon Simple Notification Service (SNS) can now be used to send notifications containing encrypted PHI/PII as part of patient care, payment processing, and mobile applications.

Amazon Cognito can now be used to authenticate users into mobile patient portal and payment processing applications that use PHI/PII identifiers for accounts.

Additional HIPAA Resources
Here are some additional resources that will help you to build applications that comply with HIPAA and HITECH:

Keep in Touch
In order to make use of any AWS service in any manner that involves PHI, you must first enter into an AWS Business Associate Addendum (BAA). You can contact us to start the process.

Jeff;

DevOps Practices- Two New Webinars with Puppet and New Relic

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/devops-practices-two-new-webinars-with-puppet-and-new-relic/

This month we are hosting two joint AWS-Partner webinars about how executing DevOps practices on AWS can automate configuration management and leave time for innovation. Many organizations adopt DevOps practices to manage their cloud and on-premises environments for greater scalability, speed, and reliability and these webinars give you a chance to hear directly from the partners and customers on how they did it.

Puppet

Puppet helped ServiceChannel automate their cloud configuration management to take advantage of the scalability of AWS, achieve greater flexibility, and improve their customers’ ability to connect and collaborate more frequently.

Webinar Topic: How ServiceChannel Automated Their AWS Environment with Puppet
Customer Presenter: Brian Engler, CIO, ServiceChannel
AWS Presenter: Kevin Cochran, Partner Solutions Architect
Partner Presenter: Chris Barker, Principal Solutions Engineer, Puppet
Time: July 20th, 2017 10am – 11am PDT | 1pm – 2pm EDT

Register

New Relic

New Relic helped MLBAM utilize the scalability of AWS and the visibility provided by New Relic to create the “gold standard” for digital streaming video infrastructure.

Webinar Topic: MLB Advanced Media: Delivering a Digital Experience to 25 Million Fans with New Relic and AWS
Customer Presenter: Christian Villoslada, VP of Software Engineering, MLBAM & Brandon San Giovanni, Senior Operations Manager, Core Media Operations, MLBAM
AWS Presenter:
Kevin Cochran, Partner Solutions Architect
Partner Presenter: Lee Atchison, Senior Director of Strategic Architecture, New Relic
Time: July 25th, 2017 10am – 11am PDT | 1pm – 2pm EDT

Register

AWS Marketplace Update – SaaS Contracts in Action

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-marketplace-update-saas-contracts-in-action/

AWS Marketplace lets AWS customers find and use products and services offered by members of the AWS Partner Network (APN). Some marketplace offerings are billed on an hourly basis, many with a cost-saving annual option designed to line up with the procurement cycles of our enterprise customers. Other offerings are available in SaaS (Software as a Service) form and are billed based on consumption units specified by the seller. The SaaS model (described in New – SaaS subscriptions on AWS Marketplace) give sellers the flexibility to bill for actual usage: number of active hosts, number of requests, GB of log files processed, and so forth.

Recently we extended the SaaS model with the addition of SaaS contracts, which my colleague Brad Lyman introduced in his post, Announcing SaaS Contracts, a Feature to Simplify SaaS Procurement on AWS Marketplace. The contracts give our customers the opportunity save money by setting up monthly subscriptions that can be expanded to cover a one, two, or three year contract term, with automatic, configurable renewals. Sellers can provide services that require up-front payment or that offer discounts in exchange for a usage commitment.

Since Brad has already covered the seller side of this powerful and flexible new model, I would like to show you what it is like to purchase a SaaS contract. Let’s say that I want to use Splunk Cloud. I simply search for it as usual:

I click on Splunk Cloud and see that it is available in SaaS Contract form:

I can also see and review the pricing options, noting that pricing varies by location, index volume, and subscription duration:

I click on Continue. Since I do not have a contract with Splunk for this software, I’ll be redirected to the vendor’s site to create one as part of the process. I choose my location, index volume, and contract duration, and opt for automatic renewal, and then click on Create Contract:

This sets up my subscription, and I need only set up my account with Splunk:

I click on Set Up Your Account and I am ready to move forward by setting up my custom URL on the Splunk site:

This feature is available now and you can start using it today.

Jeff;

 

AWS HIPAA Program Update – Dedicated Instances and Hosts Are No Longer Required

Post Syndicated from Craig Liebendorfer original https://aws.amazon.com/blogs/security/aws-hipaa-program-update-dedicated-instances-and-hosts-are-no-longer-required/

Over the years, we have seen tremendous growth in the use of the AWS Cloud for healthcare applications. Our customers and AWS Partner Network (APN) Partners who offer solutions that store, process, and transmit Protected Health Information (PHI) sign a Business Associate Addendum (BAA) with AWS. As part of the AWS HIPAA compliance program, customers and APN Partners must use a set of HIPAA Eligible Services for portions of their applications that store, process, and transmit PHI.

Recently, our HIPAA compliance program announced that those AWS customers and APN Partners who have signed a BAA with AWS are no longer required to use Amazon EC2 Dedicated Instances and Dedicated Hosts to store, process, or transmit PHI. To learn more about the announcement and some architectural optimizations you should consider making, see the full APN Blog post.

–  Craig

Announcing 5 Partners That Have Achieved AWS Service Partner Status for AWS Service Catalog

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/announcing-5-partners-that-have-achieved-aws-service-partner-status-for-aws-service-catalog/

Allison Johnson from the AWS Marketplace wrote the guest post below to share some important APN news.

-Ana


At last November’s re:Invent, we announced the AWS Service Delivery Program to help AWS customers find partners in the AWS Partner Network (APN) with expertise in specific AWS services and skills.  Today we are adding Management Tools as a new Partner Solution to the Service Delivery program, and AWS Service Catalog is the first product to launch in that category.  APN Service Catalog partners help create catalogs of IT services that are approved by the customer’s organization for use on AWS.  With AWS Service Catalog, customers and APN partners can centrally manage commonly deployed IT services to help achieve consistent governance and meet compliance requirements while enabling users to self-provision approved services.

We have an enormous APN partner ecosystem, with thousands of consulting partners, and this program simplifies the process of finding partners experienced in implementing Service Catalog.  The consulting partners we are launching this program with today are BizCloud Experts, Cloudticity, Flux7, Logicworks, and Wipro.

The process our partners go through to achieve the Service Catalog designation is not easy.  All of the partners must have publicly referenceable customers using Service Catalog, and there is a technical review to ensure that they understand Service Catalog almost as well as our own Solution Architects do!

One common theme from all of the partner’s technical applications was the requirement to help their customers meet HIPAA compliance objectives.  HIPAA requires three essential components: 1) establishment of processes, 2) enforcement of processes, and 3) separation of roles.  Service Catalog helps with all three.  Using AWS CloudFormation templates, Service Catalog Launch constraints, and identity and access management (IAM) based permission to Service Catalog portfolios, partners are able to establish and strictly enforce a process for infrastructure delivery for their customers.  Separation of roles is achieved by using Service Catalog’s launch constraints and IAM, and our partners were able to create separation of roles such that developers were agile but were only allowed to do what their policies permitted them to.

Another common theme was the use of Service Catalog for self-service discovery and launch of AWS services.  Customers can navigate to the Service Catalog to view the dashboard of products available to them, and they are empowered to launch their own resources.

Customers also use Service Catalog for standardization and central management.  The customer’s service catalog manager can manage resource updates and changes via the Service Catalog dashboard.  Tags can be standardized across instances launched from the Service Catalog.

Below are visual diagrams of Service Catalog workflows created for two of BizCloud Expert’s customers.

Ready to learn more?  If you are an AWS customer looking for a consulting partner with Service Catalog expertise, click here for more information about our launch partners.  If you are a consulting partner and would like to apply for membership, contact us.

-Allison Johnson

 

AWS Week in Review – March 6, 2017

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-week-in-review-march-6-2017/

This edition includes all of our announcements, content from all of our blogs, and as much community-generated AWS content as I had time for!

Monday

March 6

Tuesday

March 7

Wednesday

March 8

Thursday

March 9

Friday

March 10

Saturday

March 11

Sunday

March 12

Jeff;

 

AWS and Healthcare: View From the Floor of HIMSS17

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/aws-and-healthcare-view-from-the-floor-of-himss17/

Jordin Green is our guest writer today, with an inside view from the floor of HIMSS17.

-Ana


HIMMS1Empathy. It’s not always a word you hear associated with technology but one might argue it should be a central tenet for the application of technology in healthcare, and I was reminded of that fact as I wandered the halls representing AWS at HIMSS17, the Healthcare Information and Management System Society annual meeting.

At Amazon, we’re taught to obsess over our customers, but this obsession takes on a new level of responsibility when those customers are directly working on improving the lives of patients and the overall wellness of society. Thinking about the challenges that healthcare professionals are dealing with every day drives home how important it is for AWS to ensure that using the cloud in healthcare is as frictionless as possible. So with that in mind I wanted to share some of the things I saw in and around HIMSS17 regarding healthcare and AWS.

I started my week at the HIMSS Cloud Computing Forum, which was a new full-day HIMSS pre-day focused on educating the healthcare community on cloud. I was particularly struck by the breadth of cloud use cases being explored throughout the industry, even compared to a few years ago. The program featured presentations on cloud-based care coordination, precision medicine, and security. Perhaps one of the most interesting presentations came from Jessica Kahn from the Center for Medicare & Medicaid Services (CMS), talking about the analytics platform that CMS has built in the cloud along with Nuna. Jessica talked about how a cloud-based platform allows CMS to make decisions based on data that is a month old, rather than a year or years old. Additionally, using an automated, rules-based approach, policymakers can directly query the data without having to rely on developers, bringing agility. Nuna has already gained a number of insights from hosting this de-identified Medicaid data for policy research, and is now looking to expand its services to private insurance.

The exhibition opened on Monday, and I was really excited to talk to customers about the new Healthcare & Life Sciences category in the AWS Marketplace. AWS Marketplace is a managed and curated software catalog that helps customers innovate faster and reduce costs, by making it easy to discover, evaluate, procure, immediately deploy and manage 3rd party software solutions. When a customer purchases software via the Marketplace, all of the infrastructure needed to run on AWS is deployed automatically, using the same pay-as-you-go pricing model that AWS uses. Creation of a dedicated category of healthcare is a huge step forward in making it easier for our customers to deploy cloud-based solutions. Our new category features telehealth solutions, products for managing HIPAA compliance, and products that can be used for revenue cycle management from AWS Partner Network (APN) such as PokitDok. We’re just getting started with this category; look for new additions throughout the year.

Later in the week, I tried to spend time at the number of APN Partners exhibiting at HIMSS this year, and it’s safe to say our ecosystem also had lots of moments to shine. Orion Health announced that they will migrate their Amadeus precision medicine platform to the AWS Cloud. Orion has been deploying on top of AWS for a while now, including notably the California-wide Health Information Exchange CalINDEX. Amadeus currently manages 110 million patient records; the migration will represent a significant volume of clinical data running on AWS. New APN Partner Merck announced a new Alexa skill challenge, asking developers to come up with new, innovative ways to use Alexa in the management of chronic disease. Healthcare Competency Partner ClearDATA announced its new fully-managed Containers-as-a-Service product, which simplifies development of healthcare applications by providing developers with a HIPAA-compliant environment for building, testing, and deployment.

This is only a small sample of the activity going on at HIMSS this year, and it’s impossible to capture everything in one post. You learn more about healthcare on AWS on our Cloud Computing in Healthcare page. Nonetheless, after spending four days diving in to healthcare IT, it was great to see how AWS is enabling our healthcare customers and partners deliver solutions that are impacting millions of lives across the globe.

Jordin Green

AWS Week in Review – February 27, 2016

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-week-in-review-february-27-2016/

This edition includes all of our announcements, content from all of our blogs, and as much community-generated AWS content as I had time for. Going forward I hope to bring back the other sections, as soon as I get my tooling and automation into better shape.

Monday

February 27

Tuesday

February 28

Wednesday

March 1

Thursday

March 2

Friday

March 3

Saturday

March 4

Sunday

March 5

Jeff;

 

AWS Week in Review – February 20, 2017

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-week-in-review-february-20-2017/

By popular demand, I am producing this “micro” version of the AWS Week in Review. I have included all of our announcements, content from all of our blogs, and as much community-generated AWS content as I had time for. Going forward I hope to bring back the other sections, as soon as I get my tooling and automation into better shape.

Monday

February 20

Tuesday

February 21

Wednesday

February 22

Thursday

February 23

Friday

February 24

Saturday

February 25

Jeff;