Tag Archives: database

How to create and retrieve secrets managed in AWS Secrets Manager using AWS CloudFormation template

Post Syndicated from Apurv Awasthi original https://aws.amazon.com/blogs/security/how-to-create-and-retrieve-secrets-managed-in-aws-secrets-manager-using-aws-cloudformation-template/

AWS Secrets Manager now integrates with AWS CloudFormation so you can create and retrieve secrets securely using CloudFormation. This integration makes it easier to automate provisioning your AWS infrastructure. For example, without any code changes, you can generate unique secrets for your resources with every execution of your CloudFormation template. This also improves the security of your infrastructure by storing secrets securely, encrypting automatically, and enabling rotation more easily.

Secrets Manager helps you protect the secrets needed to access your applications, services, and IT resources. In this post, I show how you can get the benefits of Secrets Manager for resources provisioned through CloudFormation. First, I describe the new Secrets Manager resource types supported in CloudFormation. Next, I show a sample CloudFormation template that launches a MySQL database on Amazon Relational Database Service (RDS). This template uses the new resource types to create, rotate, and retrieve the credentials (user name and password) of the database superuser required to launch the MySQL database.

Why use Secrets Manager with CloudFormation?

CloudFormation helps you model your AWS resources as templates and execute these templates to provision AWS resources at scale. Some AWS resources require secrets as part of the provisioning process. For example, to provision a MySQL database, you must provide the credentials for the database superuser. You can use Secrets Manager, the AWS dedicated secrets management service, to create and manage such secrets.

Secrets Manager makes it easier to rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle. You can now reference Secrets Manager in your CloudFormation templates to create unique secrets with every invocation of your template. By default, Secrets Manager encrypts these secrets with encryption keys that you own and control. Secrets Manager ensures the secret isn’t logged or persisted by CloudFormation by using a dynamic reference to the secret. You can configure Secrets Manager to rotate your secrets automatically without disrupting your applications. Secrets Manager offers built-in integrations for rotating credentials for all Amazon RDS databases and supports extensibility with AWS Lambda so you can meet your custom rotation requirements.

New Secrets Manager resource types supported in CloudFormation

  1. AWS::SecretsManager::Secret — Create a secret and store it in Secrets Manager.
  2. AWS::SecretsManager::ResourcePolicy — Create a resource-based policy and attach it to a secret. Resource-based policies enable you to control access to secrets.
  3. AWS::SecretsManager::SecretTargetAttachment — Configure Secrets Manager to rotate the secret automatically.
  4. AWS::SecretsManager::RotationSchedule — Define the Lambda function that will be used to rotate the secret.

How to use Secrets Manager in CloudFormation

Now that you’re familiar with the new Secrets Manager resource types supported in CloudFormation, I’ll show how you can use these in a CloudFormation template. I will use a sample template that creates a MySQL database in Amazon RDS and uses Secrets Manager to create the credentials for the superuser. The template also configures the secret to rotate every 30 days automatically.

  1. Create a stack on the AWS CloudFormation console by copying the following sample template.
    
    ---
    Description: "How to create and retrieve secrets securely using an AWS CloudFormation template"
    Resources:
    
    # Create a secret with the username admin and a randomly generated password in JSON.  
      MyRDSInstanceRotationSecret:
        Type: AWS::SecretsManager::Secret
        Properties:
          Description: 'This is the secret for my RDS instance'
          GenerateSecretString:
            SecretStringTemplate: '{"username": "admin"}'
            GenerateStringKey: 'password'
            PasswordLength: 16
            ExcludeCharacters: '"@/'
    
    
    
    # Create a MySQL database of size t2.micro.
    # The secret (username and password for the superuser) will be dynamically 
    # referenced. This ensures CloudFormation will not log or persist the resolved 
    # value. 
      MyDBInstance:
        Type: AWS::RDS::DBInstance
        Properties:
          AllocatedStorage: 20
          DBInstanceClass: db.t2.micro
          Engine: mysql
          MasterUsername: !Join ['', ['{{resolve:secretsmanager:', !Ref MyRDSInstanceRotationSecret, ':SecretString:username}}' ]]
          MasterUserPassword: !Join ['', ['{{resolve:secretsmanager:', !Ref MyRDSInstanceRotationSecret, ':SecretString:password}}' ]]
          BackupRetentionPeriod: 0
          DBInstanceIdentifier: 'rotation-instance'
    
    
    
    # Update the referenced secret with properties of the RDS database.
    # This is required to enable rotation. To learn more, visit our documentation
    # https://docs.aws.amazon.com/secretsmanager/latest/userguide/rotating-secrets.html
      SecretRDSInstanceAttachment:
        Type: AWS::SecretsManager::SecretTargetAttachment
        Properties:
          SecretId: !Ref MyRDSInstanceRotationSecret
          TargetId: !Ref MyDBInstance
          TargetType: AWS::RDS::DBInstance
    
    
    
    # Schedule rotating the secret every 30 days. 
    # Note, the first rotation is triggered immediately. 
    # This enables you to verify that rotation is configured appropriately.
    # Subsequent rotations are scheduled according to the configured rotation. 
      MySecretRotationSchedule:
        Type: AWS::SecretsManager::RotationSchedule
        DependsOn: SecretRDSInstanceAttachment
        Properties:
          SecretId: !Ref MyRDSInstanceRotationSecret
          RotationLambdaARN: <% replace-with-lambda-arn %>
          RotationRules:
            AutomaticallyAfterDays: 30
     
    

  2. Next, execute the stack.
     
    Figure 1: Execute the stack

    Figure 1: Execute the stack

  3. After you execute the stack, open the RDS console to verify the database, rotation-instance, has been successfully created.
     
    Figure 2: Verify the database has been created

    Figure 2: Verify the database has been created

  4. Open the Secrets Manager console and verify the stack successfully created the secret, MyRDSInstanceRotationSecret.
     
    Figure 3: Verify the stack successfully created the secret

    Figure 3: Verify the stack successfully created the secret

Summary

I showed you how to create and retrieve secrets in CloudFormation. This improves the security of your infrastructure and makes it easier to automate infrastructure provisioning. To get started managing secrets, open the Secrets Manager console. To learn more, read How to Store, Distribute, and Rotate Credentials Securely with Secret Manager or refer to the Secrets Manager 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 Secrets Manager forum or contact AWS Support.

Want more AWS Security news? Follow us on Twitter.

Apurv Awasthi

Apurv is the product manager for credentials management services at AWS, including AWS Secrets Manager and IAM Roles. He enjoys the “Day 1” culture at Amazon because it aligns with his experience building startups in the sports and recruiting industries. Outside of work, Apurv enjoys hiking. He holds an MBA from UCLA and an MS in computer science from University of Kentucky.

Learn about AWS – November AWS Online Tech Talks

Post Syndicated from Robin Park original https://aws.amazon.com/blogs/aws/learn-about-aws-november-aws-online-tech-talks/

AWS Tech Talks

AWS Online Tech Talks are live, online presentations that cover a broad range of topics at varying technical levels. Join us this month to learn about AWS services and solutions. We’ll have experts online to help answer any questions you may have.

Featured this month! Check out the tech talks: Virtual Hands-On Workshop: Amazon Elasticsearch Service – Analyze Your CloudTrail Logs, AWS re:Invent: Know Before You Go and AWS Office Hours: Amazon GuardDuty Tips and Tricks.

Register today!

Note – All sessions are free and in Pacific Time.

Tech talks this month:

AR/VR

November 13, 2018 | 11:00 AM – 12:00 PM PTHow to Create a Chatbot Using Amazon Sumerian and Sumerian Hosts – Learn how to quickly and easily create a chatbot using Amazon Sumerian & Sumerian Hosts.

Compute

November 19, 2018 | 11:00 AM – 12:00 PM PTUsing Amazon Lightsail to Create a Database – Learn how to set up a database on your Amazon Lightsail instance for your applications or stand-alone websites.

November 21, 2018 | 09:00 AM – 10:00 AM PTSave up to 90% on CI/CD Workloads with Amazon EC2 Spot Instances – Learn how to automatically scale a fleet of Spot Instances with Jenkins and EC2 Spot Plug-In.

Containers

November 13, 2018 | 09:00 AM – 10:00 AM PTCustomer Showcase: How Portal Finance Scaled Their Containerized Application Seamlessly with AWS Fargate – Learn how to scale your containerized applications without managing servers and cluster, using AWS Fargate.

November 14, 2018 | 11:00 AM – 12:00 PM PTCustomer Showcase: How 99designs Used AWS Fargate and Datadog to Manage their Containerized Application – Learn how 99designs scales their containerized applications using AWS Fargate.

November 21, 2018 | 11:00 AM – 12:00 PM PTMonitor the World: Meaningful Metrics for Containerized Apps and Clusters – Learn about metrics and tools you need to monitor your Kubernetes applications on AWS.

Data Lakes & Analytics

November 12, 2018 | 01:00 PM – 01:45 PM PTSearch Your DynamoDB Data with Amazon Elasticsearch Service – Learn the joint power of Amazon Elasticsearch Service and DynamoDB and how to set up your DynamoDB tables and streams to replicate your data to Amazon Elasticsearch Service.

November 13, 2018 | 01:00 PM – 01:45 PM PTVirtual Hands-On Workshop: Amazon Elasticsearch Service – Analyze Your CloudTrail Logs – Get hands-on experience and learn how to ingest and analyze CloudTrail logs using Amazon Elasticsearch Service.

November 14, 2018 | 01:00 PM – 01:45 PM PTBest Practices for Migrating Big Data Workloads to AWS – Learn how to migrate analytics, data processing (ETL), and data science workloads running on Apache Hadoop, Spark, and data warehouse appliances from on-premises deployments to AWS.

November 15, 2018 | 11:00 AM – 11:45 AM PTBest Practices for Scaling Amazon Redshift – Learn about the most common scalability pain points with analytics platforms and see how Amazon Redshift can quickly scale to fulfill growing analytical needs and data volume.

Databases

November 12, 2018 | 11:00 AM – 11:45 AM PTModernize your SQL Server 2008/R2 Databases with AWS Database Services – As end of extended Support for SQL Server 2008/ R2 nears, learn how AWS’s portfolio of fully managed, cost effective databases, and easy-to-use migration tools can help.

DevOps

November 16, 2018 | 09:00 AM – 09:45 AM PTBuild and Orchestrate Serverless Applications on AWS with PowerShell – Learn how to build and orchestrate serverless applications on AWS with AWS Lambda and PowerShell.

End-User Computing

November 19, 2018 | 01:00 PM – 02:00 PM PTWork Without Workstations with AppStream 2.0 – Learn how to work without workstations and accelerate your engineering workflows using AppStream 2.0.

Enterprise & Hybrid

November 19, 2018 | 09:00 AM – 10:00 AM PTEnterprise DevOps: New Patterns of Efficiency – Learn how to implement “Enterprise DevOps” in your organization through building a culture of inclusion, common sense, and continuous improvement.

November 20, 2018 | 11:00 AM – 11:45 AM PTAre Your Workloads Well-Architected? – Learn how to measure and improve your workloads with AWS Well-Architected best practices.

IoT

November 16, 2018 | 01:00 PM – 02:00 PM PTPushing Intelligence to the Edge in Industrial Applications – Learn how GE uses AWS IoT for industrial use cases, including 3D printing and aviation.

Machine Learning

November 12, 2018 | 09:00 AM – 09:45 AM PTAutomate for Efficiency with Amazon Transcribe and Amazon Translate – Learn how you can increase efficiency and reach of your operations with Amazon Translate and Amazon Transcribe.

Mobile

November 20, 2018 | 01:00 PM – 02:00 PM PTGraphQL Deep Dive – Designing Schemas and Automating Deployment – Get an overview of the basics of how GraphQL works and dive into different schema designs, best practices, and considerations for providing data to your applications in production.

re:Invent

November 9, 2018 | 08:00 AM – 08:30 AM PTEpisode 7: Getting Around the re:Invent Campus – Learn how to efficiently get around the re:Invent campus using our new mobile app technology. Make sure you arrive on time and never miss a session.

November 14, 2018 | 08:00 AM – 08:30 AM PTEpisode 8: Know Before You Go – Learn about all final details you need to know before you arrive in Las Vegas for AWS re:Invent!

Security, Identity & Compliance

November 16, 2018 | 11:00 AM – 12:00 PM PTAWS Office Hours: Amazon GuardDuty Tips and Tricks – Join us for office hours and get the latest tips and tricks for Amazon GuardDuty from AWS Security experts.

Serverless

November 14, 2018 | 09:00 AM – 10:00 AM PTServerless Workflows for the Enterprise – Learn how to seamlessly build and deploy serverless applications across multiple teams in large organizations.

Storage

November 15, 2018 | 01:00 PM – 01:45 PM PTMove From Tape Backups to AWS in 30 Minutes – Learn how to switch to cloud backups easily with AWS Storage Gateway.

November 20, 2018 | 09:00 AM – 10:00 AM PTDeep Dive on Amazon S3 Security and Management – Amazon S3 provides some of the most enhanced data security features available in the cloud today, including access controls, encryption, security monitoring, remediation, and security standards and compliance certifications.

Performance matters: Amazon Redshift is now up to 3.5x faster for real-world workloads

Post Syndicated from Ayush Jain original https://aws.amazon.com/blogs/big-data/performance-matters-amazon-redshift-is-now-up-to-3-5x-faster-for-real-world-workloads/

Since we launched Amazon Redshift, thousands of customers have trusted us to get uncompromising speed for their most complex analytical workloads. Over the course of 2017, our customers benefited from a 3x to 5x performance gain, resulting from short query acceleration, result caching, late materialization, and many other under-the-hood improvements. In this post, we highlight recent improvements to Amazon Redshift and how our continued focus on performance enhancements is benefiting customers. We also discuss performance testing derived from industry-standard benchmarks that help us measure the impact of these ongoing improvements.

Recent performance improvements

With the largest number of data warehousing deployments in the cloud, we have the ability to analyze usage patterns across a variety of analytical workloads and uncover opportunities to improve performance. We leverage these insights to deliver improvements that seamlessly benefit thousands of customers. Major improvements in performance over the past six months include the following:

  • Improved resource management for memory-intensive queries: Amazon Redshift improved how joins and aggregations consume and reserve memory. This improved cache efficiency for the majority of the hash tables and reduced spilling for memory-intensive joins and aggregations by up to 1.6x.
  • Improved performance for commits: As a central component of write transactions, commit has a direct impact on the performance of data update and data ingestion workloads, such as ETL (extract, transform, and load) jobs. Since November 2017, we’ve delivered a series of commit performance optimizations such as batching multiple commits in a single operation, improved usage of commit locks, and a locality-aware metadata defragmenter. These and other related optimizations have resulted in a 4x commit time reduction on average for HDD-based clusters. For heavy transactions (the top 5 percent of commit operations in Amazon Redshift), the delivered optimizations resulted in a 7.5x improvement.
  • Improved performance for repeated queries: With Amazon Redshift’s result caching, dashboards, visualization, and business intelligence (BI) tools that execute queries repeatedly now see a significant boost in performance. In addition, result caching frees up resources that can improve the performance of all other queries.
  • Query processing improvements: Amazon Redshift now performs 2x–6x faster for scenarios such as repeated subqueries, advanced analytics functions with predicates, and complex query plans by eliminating duplicate work and streamlining steps.
  • Faster string manipulation: Amazon Redshift yields 5x better performance for frequently used string functions because of more efficient code generation techniques.

We’ve also complemented these out-of-the-box improvements with tailored recommendations to help you get better performance at a lower cost with Amazon Redshift Advisor. Advisor has already provided close to 50,000 recommendations since it launched in July 2018.

All of these optimizations have transparently boosted customers’ ability to get faster insights from their AWS analytics platform and saved thousands of hours of execution time on a daily basis. This applies to even the largest deployments, where customers have multiple petabytes of data in Redshift clusters, and seamless access to even larger data volumes in their Amazon S3 data lakes with Amazon Redshift Spectrum. “Redshift’s query performance and scalability has been increasing, even though our data has grown.” said Minero Aoki, Senior Data Engineer, Cookpad Inc. “In the last 10 months, we have seen commit performance increase by 500% without any increase in cost.”

Using benchmarks to measure success

To measure the impact of these ongoing improvements, we measure performance on a nightly basis and run queries derived from industry-standard benchmarks such as TPC-DS. We also occasionally benchmark Amazon Redshift against other data warehouse services. We set up these measurements to reflect our customers’ real-world usage, as highlighted earlier. This enables us to accurately gauge whether Amazon Redshift is getting better with each release, which happens every two weeks.

Comparing Amazon Redshift releases over the past few months, we observed that Amazon Redshift is now 3.5x faster versus six months ago, running all 99 queries derived from the TPC-DS benchmark. This is shown in the following chart.

Note: We used a Cloud DW benchmark derived from TPC-DS for this study. As such, the Cloud DW benchmark is not comparable to published TPC-DS results. TPC Benchmark and TPC-DS are trademarks of the Transaction Processing Performance Council.

For this post, we also compared the latest Amazon Redshift release with Microsoft Azure SQL Data Warehouse using the Cloud DW benchmark derived from TPC-DS. Queries ran against a 3 TB dataset on a 4-node cluster on both services, using dc2.8xlarge for Amazon Redshift and DW2000c Gen2 for Azure SQL Data Warehouse. We could not run a larger dataset because Azure could not allocate the DW15000c cluster required for a 30 TB dataset owing to capacity constraints at the time of publishing.

We observed that Amazon Redshift is 15x faster than Azure SQL Data Warehouse running all 99 queries with one user, and 14x faster with four concurrent users. There were a couple of outlier queries that took Azure SQL Data Warehouse several hours to complete. Excluding the two long running queries, Amazon Redshift is 2x faster than Azure SQL Data Warehouse with 1 user and 1.6x faster with four concurrent users. The following charts compare the two services.

Note: We used queries derived from TPC-DS v2.9 for this study. Amazon Redshift and Azure SQL DW do not support rollup queries, so we used TPC-DS provided variants for queries 5, 14, 18, 27, 36, 67, 70, 77, 80, and 86. We used out-of-the-box Workload Management configuration for Amazon Redshift, which allows for 5 concurrent queries, and ‘largerc’ resource class for Azure SQL DW, which has a lower limit of 4 concurrent queries. Amazon Redshift took 25 minutes to run all 99 queries, whereas Azure SQL Data Warehouse took 6.4 hours. Ignoring two queries that each took Azure SQL Data Warehouse more than 1 hour to execute (Q38 and Q67), Amazon Redshift took 22 minutes, while Azure SQL Data Warehouse took 42 minutes.

 

Evaluating Amazon Redshift

Although benchmarks against other data warehouse services are interesting, they are of limited value. First, there’s no one-size-fits-all benchmark. Each service has its unique real-world usage patterns and ways to configure and tune for them. We make a best effort to configure the services based on publicly available guidance, but we can’t guarantee optimal performance for any given service. We see this commonly with third-party benchmarks, for instance, where Amazon Redshift’s powerful distribution and sort keys are not used—even though the large majority of our customers use them.

Similarly, each benchmark query can only be run once, in contrast to real-world scenarios where 99.5 percent of queries we observe have components that can be found in the compilation cache (Amazon Redshift generates and compiles code for each query execution plan. The compiled code segments are stored in a least recently used cache and shared across sessions in a cluster). In other words, they are similar to queries that were run previously. So, the query run times measured by benchmarking studies can end up over-indexing on compilation times, which might not indicate the actual performance you can expect to get.

Secondly, these studies are, by necessity, a point-in-time assessment. As cloud vendors update and evolve their service, benchmark numbers might already be obsolete by the time they’re published.

Therefore, we don’t recommend that you make product selection decisions based on these benchmarks because your data and your query workloads have their own unique characteristics. If you’re evaluating Amazon Redshift for your analytics platform, we have created a Proof of Concept guide to help. You can also request assistance from us, or work with one of our System Integration and Consulting Partners and make a data-driven decision.

Finally, we invite you to watch the recent Fireside chat webinar and join us at re:Invent 2018 in Las Vegas, where we have a ton of exciting news to share with you. Happy querying!

If you would like instruction to reproduce the benchmark, please contact us at [email protected]. If you have questions or suggestions, please comment below.


About the Authors

Ayush Jain is a Product Marketer at Amazon Web Services. He loves growing cloud services and helping customers get more value from the cloud deployments. He has several years of experience in Software Development, Product Management and Product Marketing in developer and data services.

 

 

 

Mostafa Mokhtar is an engineer working on Redshift performance. Previously, he held similar roles at Cloudera, Hortonworks and on the SQL Server team at Microsoft.

 

How to use AWS Secrets Manager to rotate credentials for all Amazon RDS database types, including Oracle

Post Syndicated from Apurv Awasthi original https://aws.amazon.com/blogs/security/how-to-use-aws-secrets-manager-rotate-credentials-amazon-rds-database-types-oracle/

You can now use AWS Secrets Manager to rotate credentials for Oracle, Microsoft SQL Server, or MariaDB databases hosted on Amazon Relational Database Service (Amazon RDS) automatically. Previously, I showed how to rotate credentials for a MySQL database hosted on Amazon RDS automatically with AWS Secrets Manager. With today’s launch, you can use Secrets Manager to automatically rotate credentials for all types of databases hosted on Amazon RDS.

In this post, I review the key features of Secrets Manager. You’ll then learn:

  1. How to store the database credential for the superuser of an Oracle database hosted on Amazon RDS
  2. How to store the Oracle database credential used by an application
  3. How to configure Secrets Manager to rotate both Oracle credentials automatically on a schedule that you define

Key features of Secrets Manager

AWS Secrets Manager makes it easier to rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle. The key features of this service include the ability to:

  1. Secure and manage secrets centrally. You can store, view, and manage all your secrets centrally. By default, Secrets Manager encrypts these secrets with encryption keys that you own and control. You can use fine-grained IAM policies or resource-based policies to control access to your secrets. You can also tag secrets to help you discover, organize, and control access to secrets used throughout your organization.
  2. Rotate secrets safely. You can configure Secrets Manager to rotate secrets automatically without disrupting your applications. Secrets Manager offers built-in integrations for rotating credentials for all Amazon RDS databases (MySQL, PostgreSQL, Oracle, Microsoft SQL Server, MariaDB, and Amazon Aurora.) You can also extend Secrets Manager to meet your custom rotation requirements by creating an AWS Lambda function to rotate other types of secrets.
  3. Transmit securely. Secrets are transmitted securely over Transport Layer Security (TLS) protocol 1.2. You can also use Secrets Manager with Amazon Virtual Private Cloud (Amazon VPC) endpoints powered by AWS Privatelink to keep this communication within the AWS network and help meet your compliance and regulatory requirements to limit public internet connectivity.
  4. Pay as you go. Pay for the secrets you store in Secrets Manager and for the use of these secrets; there are no long-term contracts, licensing fees, or infrastructure and personnel costs. For example, a typical production-scale web application will generate an estimated monthly bill of $6. If you follow along the instructions in this blog post, your estimated monthly bill for Secrets Manager will be $1. Note: you may incur additional charges for using Amazon RDS and Amazon Lambda, if you’ve already consumed the free tier for these services.

Now that you’re familiar with Secrets Manager features, I’ll show you how to store and automatically rotate credentials for an Oracle database hosted on Amazon RDS. I divided these instructions into three phases:

  1. Phase 1: Store and configure rotation for the superuser credential
  2. Phase 2: Store and configure rotation for the application credential
  3. Phase 3: Retrieve the credential from Secrets Manager programmatically

Prerequisites

To follow along, your AWS Identity and Access Management (IAM) principal (user or role) requires the SecretsManagerReadWrite AWS managed policy to store the secrets. Your principal also requires the IAMFullAccess AWS managed policy to create and configure permissions for the IAM role used by Lambda for executing rotations. You can use IAM permissions boundaries to grant an employee the ability to configure rotation without also granting them full administrative access to your account.

Phase 1: Store and configure rotation for the superuser credential

From the Secrets Manager console, on the right side, select Store a new secret.

Since I’m storing credentials for database hosted on Amazon RDS, I select Credentials for RDS database. Next, I input the user name and password for the superuser. I start by securing the superuser because it’s the most powerful database credential and has full access to the database.
 

Figure 1: For "Select secret type," choose "Credentials for RDS database"

Figure 1: For “Select secret type,” choose “Credentials for RDS database”

For this example, I choose to use the default encryption settings. Secrets Manager will encrypt this secret using the Secrets Manager DefaultEncryptionKey in this account. Alternatively, I can choose to encrypt using a customer master key (CMK) that I have stored in AWS Key Management Service (AWS KMS). To learn more, read the Using Your AWS KMS CMK documentation.
 

Figure 2: Choose either DefaultEncryptionKey or use a CMK

Figure 2: Choose either DefaultEncryptionKey or use a CMK

Next, I view the list of Amazon RDS instances in my account and select the database this credential accesses. For this example, I select the DB instance oracle-rds-database from the list, and then I select Next.

I then specify values for Secret name and Description. For this example, I use Database/Development/Oracle-Superuser as the name and enter a description of this secret, and then select Next.
 

Figure 3: Provide values for "Secret name" and "Description"

Figure 3: Provide values for “Secret name” and “Description”

Since this database is not yet being used, I choose to enable rotation. To do so, I select Enable automatic rotation, and then set the rotation interval to 60 days. Remember, if this database credential is currently being used, first update the application (see phase 3) to use Secrets Manager APIs to retrieve secrets before enabling rotation.
 

Figure 4: Select "Enable automatic rotation"

Figure 4: Select “Enable automatic rotation”

Next, Secrets Manager requires permissions to rotate this secret on my behalf. Because I’m storing the credentials for the superuser, Secrets Manager can use this credential to perform rotations. Therefore, on the same screen, I select Use a secret that I have previously stored in AWS Secrets Manager, and then select Next.

Finally, I review the information on the next screen. Everything looks correct, so I select Store. I have now successfully stored a secret in Secrets Manager.

Note: Secrets Manager will now create a Lambda function in the same VPC as my Oracle database and trigger this function periodically to change the password for the superuser. I can view the name of the Lambda function on the Rotation configuration section of the Secret Details page.

The banner on the next screen confirms that I’ve successfully configured rotation and the first rotation is in progress, which enables me to verify that rotation is functioning as expected. Secrets Manager will rotate this credential automatically every 60 days.
 

Figure 5: The confirmation notification

Figure 5: The confirmation notification

Phase 2: Store and configure rotation for the application credential

The superuser is a powerful credential that should be used only for administrative tasks. To enable your applications to access a database, create a unique database credential per application and grant these credentials limited permissions. You can use these database credentials to read or write to database tables required by the application. As a security best practice, deny the ability to perform management actions, such as creating new credentials.

In this phase, I will store the credential that my application will use to connect to the Oracle database. To get started, from the Secrets Manager console, on the right side, select Store a new secret.

Next, I select Credentials for RDS database, and input the user name and password for the application credential.

I continue to use the default encryption key. I select the DB instance oracle-rds-database, and then select Next.

I specify values for Secret Name and Description. For this example, I use Database/Development/Oracle-Application-User as the name and enter a description of this secret, and then select Next.

I now configure rotation. Once again, since my application is not using this database credential yet, I’ll configure rotation as part of storing this secret. I select Enable automatic rotation, and set the rotation interval to 60 days.

Next, Secrets Manager requires permissions to rotate this secret on behalf of my application. Earlier in the post, I mentioned that applications credentials have limited permissions and are unable to change their password. Therefore, I will use the superuser credential, Database/Development/Oracle-Superuser, that I stored in Phase 1 to rotate the application credential. With this configuration, Secrets Manager creates a clone application user.
 

Figure 6: Select the superuser credential

Figure 6: Select the superuser credential

Note: Creating a clone application user is the preferred mechanism of rotation because the old version of the secret continues to operate and handle service requests while the new version is prepared and tested. There’s no application downtime while changing between versions.

I review the information on the next screen. Everything looks correct, so I select Store. I have now successfully stored the application credential in Secrets Manager.

As mentioned in Phase 1, AWS Secrets Manager creates a Lambda function in the same VPC as the database and then triggers this function periodically to rotate the secret. Since I chose to use the existing superuser secret to rotate the application secret, I will grant the rotation Lambda function permissions to retrieve the superuser secret. To grant this permission, I first select role from the confirmation banner.
 

Figure 7: Select the "role" link that's in the confirmation notification

Figure 7: Select the “role” link that’s in the confirmation notification

Next, in the Permissions tab, I select SecretsManagerRDSMySQLRotationMultiUserRolePolicy0. Then I select Edit policy.
 

Figure 8: Edit the policy on the "Permissions" tab

Figure 8: Edit the policy on the “Permissions” tab

In this step, I update the policy (see below) and select Review policy. When following along, remember to replace the placeholder ARN-OF-SUPERUSER-SECRET with the ARN of the secret you stored in Phase 1.


{
  "Statement": [
    {
        "Effect": "Allow",
        "Action": [
            "ec2:CreateNetworkInterface",
			"ec2:DeleteNetworkInterface",
			"ec2:DescribeNetworkInterfaces",
			"ec2:DetachNetworkInterface"
		],
		"Resource": "*"
	},
	{
	    "Sid": "GrantPermissionToUse",
		"Effect": "Allow",
		"Action": [
            "secretsmanager:GetSecretValue"
        ],
		"Resource": "ARN-OF-SUPERUSER-SECRET"
	}
  ]
}

Here’s what it will look like:
 

Figure 9: Edit the policy

Figure 9: Edit the policy

Next, I select Save changes. I have now completed all the steps required to configure rotation for the application credential, Database/Development/Oracle-Application-User.

Phase 3: Retrieve the credential from Secrets Manager programmatically

Now that I have stored the secret in Secrets Manager, I add code to my application to retrieve the database credential from Secrets Manager. I use the sample code from Phase 2 above. This code sets up the client and retrieves and decrypts the secret Database/Development/Oracle-Application-User.

Remember, applications require permissions to retrieve the secret, Database/Development/Oracle-Application-User, from Secrets Manager. My application runs on Amazon EC2 and uses an IAM role to obtain access to AWS services. I attach the following policy to my IAM role. This policy uses the GetSecretValue action to grant my application permissions to read secret from Secrets Manager. This policy also uses the resource element to limit my application to read only the Database/Development/Oracle-Application-User secret from Secrets Manager. You can refer to the Secrets Manager Documentation to understand the minimum IAM permissions required to retrieve a secret.


{
 "Version": "2012-10-17",
 "Statement": {
    "Sid": "RetrieveDbCredentialFromSecretsManager",
    "Effect": "Allow",
    "Action": "secretsmanager:GetSecretValue",
    "Resource": "arn:aws:secretsmanager:<AWS-REGION>:<ACCOUNT-NUMBER>:secret: Database/Development/Oracle-Application-User     
 }
}

In the above policy, remember to replace the placeholder <AWS-REGION> with the AWS region that you’re using and the placeholder <ACCOUNT-NUMBER> with the number of your AWS account.

Summary

I explained the key benefits of Secrets Manager as they relate to RDS and showed you how to help meet your compliance requirements by configuring Secrets Manager to rotate database credentials automatically on your behalf. Secrets Manager helps you protect access to your applications, services, and IT resources without the upfront investment and on-going maintenance costs of operating your own secrets management infrastructure. To get started, visit the Secrets Manager console. To learn more, visit Secrets Manager 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 Secrets Manager forum.

Want more AWS Security news? Follow us on Twitter.

Apurv Awasthi

Apurv is the product manager for credentials management services at AWS, including AWS Secrets Manager and IAM Roles. He enjoys the “Day 1” culture at Amazon because it aligns with his experience building startups in the sports and recruiting industries. Outside of work, Apurv enjoys hiking. He holds an MBA from UCLA and an MS in computer science from University of Kentucky.

Aurora Serverless MySQL Generally Available

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/aurora-serverless-ga/

You may have heard of Amazon Aurora, a custom built MySQL and PostgreSQL compatible database born and built in the cloud. You may have also heard of serverless, which allows you to build and run applications and services without thinking about instances. These are two pieces of the growing AWS technology story that we’re really excited to be working on. Last year, at AWS re:Invent we announced a preview of a new capability for Aurora called Aurora Serverless. Today, I’m pleased to announce that Aurora Serverless for Aurora MySQL is generally available. Aurora Serverless is on-demand, auto-scaling, serverless Aurora. You don’t have to think about instances or scaling and you pay only for what you use.

This paradigm is great for applications with unpredictable load or infrequent demand. I’m excited to show you how this all works. Let me show you how to launch a serverless cluster.

Creating an Aurora Serverless Cluster

First, I’ll navigate to the Amazon Relational Database Service (RDS) console and select the Clusters sub-console. From there, I’ll click the Create database button in the top right corner to get to this screen.

From the screen above I select my engine type and click next, for now only Aurora MySQL 5.6 is supported.

Now comes the fun part. I specify my capacity type as Serverless and all of the instance selection and configuration options go away. I only have to give my cluster a name and a master username/password combo and click next.

From here I can select a number of options. I can specify the minimum and maximum number of Aurora Compute Units (ACU) to be consumed. These are billed per-second, with a 5-minute minimum, and my cluster will autoscale between the specified minimum and maximum number of ACUs. The rules and metrics for autoscaling will be automatically created by Aurora Serverless and will include CPU utilization and number of connections. When Aurora Serverless detects that my cluster needs additional capacity it will grab capacity from a warm pool of resources to meet the need. This new capacity can start serving traffic in seconds because of the separation of the compute layer and storage layer intrinsic to the design of Aurora.

The cluster can even automatically scale down to zero if my cluster isn’t seeing any activity. This is perfect for development databases that might go long periods of time with little or no use. When the cluster is paused I’m only charged for the underlying storage. If I want to manually scale up or down, pre-empting a large spike in traffic, I can easily do that with a single API call.

Finally, I click Create database in the bottom right and wait for my cluster to become available – which happens quite quickly. For now we only support a limited number of cluster parameters with plans to enable more customized options as we iterate on customer feedback.

Now, the console provides a wealth of data, similar to any other RDS database.

From here, I can connect to my cluster like any other MySQL database. I could run a tool like sysbench or mysqlslap to generate some load and trigger a scaling event or I could just wait for the service to scale down and pause.

If I scroll down or select the events subconsole I can see a few different autoscaling events happening including pausing the instance at one point.

The best part about this? When I’m done writing the blog post I don’t need to remember to shut this server down! When I’m ready to use it again I just make a connection request and my cluster starts responding in seconds.

How Aurora Serverless Works

I want to dive a bit deeper into what exactly is happening behind the scenes to enable this functionality. When you provision an Aurora Serverless database the service does a few things:

  • It creates an Aurora storage volume replicated across multiple AZs.
  • It creates an endpoint in your VPC for the application to connect to.
  • It configures a network load balancer (invisible to the customer) behind that endpoint.
  • It configures multi-tenant request routers to route database traffic to the underlying instances.
  • It provisions the initial minimum instance capacity.

 

When the cluster needs to autoscale up or down or resume after a pause, Aurora grabs capacity from a pool of already available nodes and adds them to the request routers. This process takes almost no time and since the storage is shared between nodes Aurora can scale up or down in seconds for most workloads. The service currently has autoscaling cooldown periods of 1.5 minutes for scaling up and 5 minutes for scaling down. Scaling operations are transparent to the connected clients and applications since existing connections and session state are transferred to the new nodes. The only difference with pausing and resuming is a higher latency for the first connection, typically around 25 seconds.

Available Now

Aurora Serverless for Aurora MySQL is available now in US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Ireland). If you’re interested in learning more about the Aurora engine there’s a great design paper available. If you’re interested in diving a bit deeper on exactly how Aurora Serverless works then look forward to more detail in future posts!

I personally believe this is one of the really exciting points in the evolution of the database story and I can’t wait to see what customers build with it!

Randall

AWS Online Tech Talks – July 2018

Post Syndicated from Sara Rodas original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-july-2018/

Join us this month to learn about AWS services and solutions featuring topics on Amazon EMR, Amazon SageMaker, AWS Lambda, Amazon S3, Amazon WorkSpaces, Amazon EC2 Fleet and more! We also have our third episode of the “How to re:Invent” where we’ll dive deep with the AWS Training and Certification team on Bootcamps, Hands-on Labs, and how to get AWS Certified at re:Invent. Register now! We look forward to seeing you. Please note – all sessions are free and in Pacific Time.

 

Tech talks featured this month:

 

Analytics & Big Data

July 23, 2018 | 11:00 AM – 12:00 PM PT – Large Scale Machine Learning with Spark on EMR – Learn how to do large scale machine learning on Amazon EMR.

July 25, 2018 | 01:00 PM – 02:00 PM PT – Introduction to Amazon QuickSight: Business Analytics for Everyone – Get an introduction to Amazon Quicksight, Amazon’s BI service.

July 26, 2018 | 11:00 AM – 12:00 PM PT – Multi-Tenant Analytics on Amazon EMR – Discover how to make an Amazon EMR cluster multi-tenant to have different processing activities on the same data lake.

 

Compute

July 31, 2018 | 11:00 AM – 12:00 PM PT – Accelerate Machine Learning Workloads Using Amazon EC2 P3 Instances – Learn how to use Amazon EC2 P3 instances, the most powerful, cost-effective and versatile GPU compute instances available in the cloud.

August 1, 2018 | 09:00 AM – 10:00 AM PT – Technical Deep Dive on Amazon EC2 Fleet – Learn how to launch workloads across instance types, purchase models, and AZs with EC2 Fleet to achieve the desired scale, performance and cost.

 

Containers

July 25, 2018 | 11:00 AM – 11:45 AM PT – How Harry’s Shaved Off Their Operational Overhead by Moving to AWS Fargate – Learn how Harry’s migrated their messaging workload to Fargate and reduced message processing time by more than 75%.

 

Databases

July 23, 2018 | 01:00 PM – 01:45 PM PT – Purpose-Built Databases: Choose the Right Tool for Each Job – Learn about purpose-built databases and when to use which database for your application.

July 24, 2018 | 11:00 AM – 11:45 AM PT – Migrating IBM Db2 Databases to AWS – Learn how to migrate your IBM Db2 database to the cloud database of your choice.

 

DevOps

July 25, 2018 | 09:00 AM – 09:45 AM PT – Optimize Your Jenkins Build Farm – Learn how to optimize your Jenkins build farm using the plug-in for AWS CodeBuild.

 

Enterprise & Hybrid

July 31, 2018 | 09:00 AM – 09:45 AM PT – Enable Developer Productivity with Amazon WorkSpaces – Learn how your development teams can be more productive with Amazon WorkSpaces.

August 1, 2018 | 11:00 AM – 11:45 AM PT – Enterprise DevOps: Applying ITIL to Rapid Innovation – Innovation doesn’t have to equate to more risk for your organization. Learn how Enterprise DevOps delivers agility while maintaining governance, security and compliance.

 

IoT

July 30, 2018 | 01:00 PM – 01:45 PM PT – Using AWS IoT & Alexa Skills Kit to Voice-Control Connected Home Devices – Hands-on workshop that covers how to build a simple backend service using AWS IoT to support an Alexa Smart Home skill.

 

Machine Learning

July 23, 2018 | 09:00 AM – 09:45 AM PT – Leveraging ML Services to Enhance Content Discovery and Recommendations – See how customers are using computer vision and language AI services to enhance content discovery & recommendations.

July 24, 2018 | 09:00 AM – 09:45 AM PT – Hyperparameter Tuning with Amazon SageMaker’s Automatic Model Tuning – Learn how to use Automatic Model Tuning with Amazon SageMaker to get the best machine learning model for your datasets, to tune hyperparameters.

July 26, 2018 | 09:00 AM – 10:00 AM PT – Build Intelligent Applications with Machine Learning on AWS – Learn how to accelerate development of AI applications using machine learning on AWS.

 

re:Invent

July 18, 2018 | 08:00 AM – 08:30 AM PT – Episode 3: Training & Certification Round-Up – Join us as we dive deep with the AWS Training and Certification team on Bootcamps, Hands-on Labs, and how to get AWS Certified at re:Invent.

 

Security, Identity, & Compliance

July 30, 2018 | 11:00 AM – 11:45 AM PT – Get Started with Well-Architected Security Best Practices – Discover and walk through essential best practices for securing your workloads using a number of AWS services.

 

Serverless

July 24, 2018 | 01:00 PM – 02:00 PM PT – Getting Started with Serverless Computing Using AWS Lambda – Get an introduction to serverless and how to start building applications with no server management.

 

Storage

July 30, 2018 | 09:00 AM – 09:45 AM PT – Best Practices for Security in Amazon S3 – Learn about Amazon S3 security fundamentals and lots of new features that help make security simple.

AWS Online Tech Talks – June 2018

Post Syndicated from Devin Watson original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-june-2018/

AWS Online Tech Talks – June 2018

Join us this month to learn about AWS services and solutions. New this month, we have a fireside chat with the GM of Amazon WorkSpaces and our 2nd episode of the “How to re:Invent” series. We’ll also cover best practices, deep dives, use cases and more! Join us and register today!

Note – All sessions are free and in Pacific Time.

Tech talks featured this month:

 

Analytics & Big Data

June 18, 2018 | 11:00 AM – 11:45 AM PTGet Started with Real-Time Streaming Data in Under 5 Minutes – Learn how to use Amazon Kinesis to capture, store, and analyze streaming data in real-time including IoT device data, VPC flow logs, and clickstream data.
June 20, 2018 | 11:00 AM – 11:45 AM PT – Insights For Everyone – Deploying Data across your Organization – Learn how to deploy data at scale using AWS Analytics and QuickSight’s new reader role and usage based pricing.

 

AWS re:Invent
June 13, 2018 | 05:00 PM – 05:30 PM PTEpisode 2: AWS re:Invent Breakout Content Secret Sauce – Hear from one of our own AWS content experts as we dive deep into the re:Invent content strategy and how we maintain a high bar.
Compute

June 25, 2018 | 01:00 PM – 01:45 PM PTAccelerating Containerized Workloads with Amazon EC2 Spot Instances – Learn how to efficiently deploy containerized workloads and easily manage clusters at any scale at a fraction of the cost with Spot Instances.

June 26, 2018 | 01:00 PM – 01:45 PM PTEnsuring Your Windows Server Workloads Are Well-Architected – Get the benefits, best practices and tools on running your Microsoft Workloads on AWS leveraging a well-architected approach.

 

Containers
June 25, 2018 | 09:00 AM – 09:45 AM PTRunning Kubernetes on AWS – Learn about the basics of running Kubernetes on AWS including how setup masters, networking, security, and add auto-scaling to your cluster.

 

Databases

June 18, 2018 | 01:00 PM – 01:45 PM PTOracle to Amazon Aurora Migration, Step by Step – Learn how to migrate your Oracle database to Amazon Aurora.
DevOps

June 20, 2018 | 09:00 AM – 09:45 AM PTSet Up a CI/CD Pipeline for Deploying Containers Using the AWS Developer Tools – Learn how to set up a CI/CD pipeline for deploying containers using the AWS Developer Tools.

 

Enterprise & Hybrid
June 18, 2018 | 09:00 AM – 09:45 AM PTDe-risking Enterprise Migration with AWS Managed Services – Learn how enterprise customers are de-risking cloud adoption with AWS Managed Services.

June 19, 2018 | 11:00 AM – 11:45 AM PTLaunch AWS Faster using Automated Landing Zones – Learn how the AWS Landing Zone can automate the set up of best practice baselines when setting up new

 

AWS Environments

June 21, 2018 | 11:00 AM – 11:45 AM PTLeading Your Team Through a Cloud Transformation – Learn how you can help lead your organization through a cloud transformation.

June 21, 2018 | 01:00 PM – 01:45 PM PTEnabling New Retail Customer Experiences with Big Data – Learn how AWS can help retailers realize actual value from their big data and deliver on differentiated retail customer experiences.

June 28, 2018 | 01:00 PM – 01:45 PM PTFireside Chat: End User Collaboration on AWS – Learn how End User Compute services can help you deliver access to desktops and applications anywhere, anytime, using any device.
IoT

June 27, 2018 | 11:00 AM – 11:45 AM PTAWS IoT in the Connected Home – Learn how to use AWS IoT to build innovative Connected Home products.

 

Machine Learning

June 19, 2018 | 09:00 AM – 09:45 AM PTIntegrating Amazon SageMaker into your Enterprise – Learn how to integrate Amazon SageMaker and other AWS Services within an Enterprise environment.

June 21, 2018 | 09:00 AM – 09:45 AM PTBuilding Text Analytics Applications on AWS using Amazon Comprehend – Learn how you can unlock the value of your unstructured data with NLP-based text analytics.

 

Management Tools

June 20, 2018 | 01:00 PM – 01:45 PM PTOptimizing Application Performance and Costs with Auto Scaling – Learn how selecting the right scaling option can help optimize application performance and costs.

 

Mobile
June 25, 2018 | 11:00 AM – 11:45 AM PTDrive User Engagement with Amazon Pinpoint – Learn how Amazon Pinpoint simplifies and streamlines effective user engagement.

 

Security, Identity & Compliance

June 26, 2018 | 09:00 AM – 09:45 AM PTUnderstanding AWS Secrets Manager – Learn how AWS Secrets Manager helps you rotate and manage access to secrets centrally.
June 28, 2018 | 09:00 AM – 09:45 AM PTUsing Amazon Inspector to Discover Potential Security Issues – See how Amazon Inspector can be used to discover security issues of your instances.

 

Serverless

June 19, 2018 | 01:00 PM – 01:45 PM PTProductionize Serverless Application Building and Deployments with AWS SAM – Learn expert tips and techniques for building and deploying serverless applications at scale with AWS SAM.

 

Storage

June 26, 2018 | 11:00 AM – 11:45 AM PTDeep Dive: Hybrid Cloud Storage with AWS Storage Gateway – Learn how you can reduce your on-premises infrastructure by using the AWS Storage Gateway to connecting your applications to the scalable and reliable AWS storage services.
June 27, 2018 | 01:00 PM – 01:45 PM PTChanging the Game: Extending Compute Capabilities to the Edge – Discover how to change the game for IIoT and edge analytics applications with AWS Snowball Edge plus enhanced Compute instances.
June 28, 2018 | 11:00 AM – 11:45 AM PTBig Data and Analytics Workloads on Amazon EFS – Get best practices and deployment advice for running big data and analytics workloads on Amazon EFS.

Build your own weather station with our new guide!

Post Syndicated from Richard Hayler original https://www.raspberrypi.org/blog/build-your-own-weather-station/

One of the most common enquiries I receive at Pi Towers is “How can I get my hands on a Raspberry Pi Oracle Weather Station?” Now the answer is: “Why not build your own version using our guide?”

Build Your Own weather station kit assembled

Tadaaaa! The BYO weather station fully assembled.

Our Oracle Weather Station

In 2016 we sent out nearly 1000 Raspberry Pi Oracle Weather Station kits to schools from around the world who had applied to be part of our weather station programme. In the original kit was a special HAT that allows the Pi to collect weather data with a set of sensors.

The original Raspberry Pi Oracle Weather Station HAT – Build Your Own Raspberry Pi weather station

The original Raspberry Pi Oracle Weather Station HAT

We designed the HAT to enable students to create their own weather stations and mount them at their schools. As part of the programme, we also provide an ever-growing range of supporting resources. We’ve seen Oracle Weather Stations in great locations with a huge differences in climate, and they’ve even recorded the effects of a solar eclipse.

Our new BYO weather station guide

We only had a single batch of HATs made, and unfortunately we’ve given nearly* all the Weather Station kits away. Not only are the kits really popular, we also receive lots of questions about how to add extra sensors or how to take more precise measurements of a particular weather phenomenon. So today, to satisfy your demand for a hackable weather station, we’re launching our Build your own weather station guide!

Build Your Own Raspberry Pi weather station

Fun with meteorological experiments!

Our guide suggests the use of many of the sensors from the Oracle Weather Station kit, so can build a station that’s as close as possible to the original. As you know, the Raspberry Pi is incredibly versatile, and we’ve made it easy to hack the design in case you want to use different sensors.

Many other tutorials for Pi-powered weather stations don’t explain how the various sensors work or how to store your data. Ours goes into more detail. It shows you how to put together a breadboard prototype, it describes how to write Python code to take readings in different ways, and it guides you through recording these readings in a database.

Build Your Own Raspberry Pi weather station on a breadboard

There’s also a section on how to make your station weatherproof. And in case you want to move past the breadboard stage, we also help you with that. The guide shows you how to solder together all the components, similar to the original Oracle Weather Station HAT.

Who should try this build

We think this is a great project to tackle at home, at a STEM club, Scout group, or CoderDojo, and we’re sure that many of you will be chomping at the bit to get started. Before you do, please note that we’ve designed the build to be as straight-forward as possible, but it’s still fairly advanced both in terms of electronics and programming. You should read through the whole guide before purchasing any components.

Build Your Own Raspberry Pi weather station – components

The sensors and components we’re suggesting balance cost, accuracy, and easy of use. Depending on what you want to use your station for, you may wish to use different components. Similarly, the final soldered design in the guide may not be the most elegant, but we think it is achievable for someone with modest soldering experience and basic equipment.

You can build a functioning weather station without soldering with our guide, but the build will be more durable if you do solder it. If you’ve never tried soldering before, that’s OK: we have a Getting started with soldering resource plus video tutorial that will walk you through how it works step by step.

Prototyping HAT for Raspberry Pi weather station sensors

For those of you who are more experienced makers, there are plenty of different ways to put the final build together. We always like to hear about alternative builds, so please post your designs in the Weather Station forum.

Our plans for the guide

Our next step is publishing supplementary guides for adding extra functionality to your weather station. We’d love to hear which enhancements you would most like to see! Our current ideas under development include adding a webcam, making a tweeting weather station, adding a light/UV meter, and incorporating a lightning sensor. Let us know which of these is your favourite, or suggest your own amazing ideas in the comments!

*We do have a very small number of kits reserved for interesting projects or locations: a particularly cool experiment, a novel idea for how the Oracle Weather Station could be used, or places with specific weather phenomena. If have such a project in mind, please send a brief outline to [email protected], and we’ll consider how we might be able to help you.

The post Build your own weather station with our new guide! appeared first on Raspberry Pi.

Storing Encrypted Credentials In Git

Post Syndicated from Bozho original https://techblog.bozho.net/storing-encrypted-credentials-in-git/

We all know that we should not commit any passwords or keys to the repo with our code (no matter if public or private). Yet, thousands of production passwords can be found on GitHub (and probably thousands more in internal company repositories). Some have tried to fix that by removing the passwords (once they learned it’s not a good idea to store them publicly), but passwords have remained in the git history.

Knowing what not to do is the first and very important step. But how do we store production credentials. Database credentials, system secrets (e.g. for HMACs), access keys for 3rd party services like payment providers or social networks. There doesn’t seem to be an agreed upon solution.

I’ve previously argued with the 12-factor app recommendation to use environment variables – if you have a few that might be okay, but when the number of variables grow (as in any real application), it becomes impractical. And you can set environment variables via a bash script, but you’d have to store it somewhere. And in fact, even separate environment variables should be stored somewhere.

This somewhere could be a local directory (risky), a shared storage, e.g. FTP or S3 bucket with limited access, or a separate git repository. I think I prefer the git repository as it allows versioning (Note: S3 also does, but is provider-specific). So you can store all your environment-specific properties files with all their credentials and environment-specific configurations in a git repo with limited access (only Ops people). And that’s not bad, as long as it’s not the same repo as the source code.

Such a repo would look like this:

project
└─── production
|   |   application.properites
|   |   keystore.jks
└─── staging
|   |   application.properites
|   |   keystore.jks
└─── on-premise-client1
|   |   application.properites
|   |   keystore.jks
└─── on-premise-client2
|   |   application.properites
|   |   keystore.jks

Since many companies are using GitHub or BitBucket for their repositories, storing production credentials on a public provider may still be risky. That’s why it’s a good idea to encrypt the files in the repository. A good way to do it is via git-crypt. It is “transparent” encryption because it supports diff and encryption and decryption on the fly. Once you set it up, you continue working with the repo as if it’s not encrypted. There’s even a fork that works on Windows.

You simply run git-crypt init (after you’ve put the git-crypt binary on your OS Path), which generates a key. Then you specify your .gitattributes, e.g. like that:

secretfile filter=git-crypt diff=git-crypt
*.key filter=git-crypt diff=git-crypt
*.properties filter=git-crypt diff=git-crypt
*.jks filter=git-crypt diff=git-crypt

And you’re done. Well, almost. If this is a fresh repo, everything is good. If it is an existing repo, you’d have to clean up your history which contains the unencrypted files. Following these steps will get you there, with one addition – before calling git commit, you should call git-crypt status -f so that the existing files are actually encrypted.

You’re almost done. We should somehow share and backup the keys. For the sharing part, it’s not a big issue to have a team of 2-3 Ops people share the same key, but you could also use the GPG option of git-crypt (as documented in the README). What’s left is to backup your secret key (that’s generated in the .git/git-crypt directory). You can store it (password-protected) in some other storage, be it a company shared folder, Dropbox/Google Drive, or even your email. Just make sure your computer is not the only place where it’s present and that it’s protected. I don’t think key rotation is necessary, but you can devise some rotation procedure.

git-crypt authors claim to shine when it comes to encrypting just a few files in an otherwise public repo. And recommend looking at git-remote-gcrypt. But as often there are non-sensitive parts of environment-specific configurations, you may not want to encrypt everything. And I think it’s perfectly fine to use git-crypt even in a separate repo scenario. And even though encryption is an okay approach to protect credentials in your source code repo, it’s still not necessarily a good idea to have the environment configurations in the same repo. Especially given that different people/teams manage these credentials. Even in small companies, maybe not all members have production access.

The outstanding questions in this case is – how do you sync the properties with code changes. Sometimes the code adds new properties that should be reflected in the environment configurations. There are two scenarios here – first, properties that could vary across environments, but can have default values (e.g. scheduled job periods), and second, properties that require explicit configuration (e.g. database credentials). The former can have the default values bundled in the code repo and therefore in the release artifact, allowing external files to override them. The latter should be announced to the people who do the deployment so that they can set the proper values.

The whole process of having versioned environment-speific configurations is actually quite simple and logical, even with the encryption added to the picture. And I think it’s a good security practice we should try to follow.

The post Storing Encrypted Credentials In Git appeared first on Bozho's tech blog.

New – Pay-per-Session Pricing for Amazon QuickSight, Another Region, and Lots More

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-pay-per-session-pricing-for-amazon-quicksight-another-region-and-lots-more/

Amazon QuickSight is a fully managed cloud business intelligence system that gives you Fast & Easy to Use Business Analytics for Big Data. QuickSight makes business analytics available to organizations of all shapes and sizes, with the ability to access data that is stored in your Amazon Redshift data warehouse, your Amazon Relational Database Service (RDS) relational databases, flat files in S3, and (via connectors) data stored in on-premises MySQL, PostgreSQL, and SQL Server databases. QuickSight scales to accommodate tens, hundreds, or thousands of users per organization.

Today we are launching a new, session-based pricing option for QuickSight, along with additional region support and other important new features. Let’s take a look at each one:

Pay-per-Session Pricing
Our customers are making great use of QuickSight and take full advantage of the power it gives them to connect to data sources, create reports, and and explore visualizations.

However, not everyone in an organization needs or wants such powerful authoring capabilities. Having access to curated data in dashboards and being able to interact with the data by drilling down, filtering, or slicing-and-dicing is more than adequate for their needs. Subscribing them to a monthly or annual plan can be seen as an unwarranted expense, so a lot of such casual users end up not having access to interactive data or BI.

In order to allow customers to provide all of their users with interactive dashboards and reports, the Enterprise Edition of Amazon QuickSight now allows Reader access to dashboards on a Pay-per-Session basis. QuickSight users are now classified as Admins, Authors, or Readers, with distinct capabilities and prices:

Authors have access to the full power of QuickSight; they can establish database connections, upload new data, create ad hoc visualizations, and publish dashboards, all for $9 per month (Standard Edition) or $18 per month (Enterprise Edition).

Readers can view dashboards, slice and dice data using drill downs, filters and on-screen controls, and download data in CSV format, all within the secure QuickSight environment. Readers pay $0.30 for 30 minutes of access, with a monthly maximum of $5 per reader.

Admins have all authoring capabilities, and can manage users and purchase SPICE capacity in the account. The QuickSight admin now has the ability to set the desired option (Author or Reader) when they invite members of their organization to use QuickSight. They can extend Reader invites to their entire user base without incurring any up-front or monthly costs, paying only for the actual usage.

To learn more, visit the QuickSight Pricing page.

A New Region
QuickSight is now available in the Asia Pacific (Tokyo) Region:

The UI is in English, with a localized version in the works.

Hourly Data Refresh
Enterprise Edition SPICE data sets can now be set to refresh as frequently as every hour. In the past, each data set could be refreshed up to 5 times a day. To learn more, read Refreshing Imported Data.

Access to Data in Private VPCs
This feature was launched in preview form late last year, and is now available in production form to users of the Enterprise Edition. As I noted at the time, you can use it to implement secure, private communication with data sources that do not have public connectivity, including on-premises data in Teradata or SQL Server, accessed over an AWS Direct Connect link. To learn more, read Working with AWS VPC.

Parameters with On-Screen Controls
QuickSight dashboards can now include parameters that are set using on-screen dropdown, text box, numeric slider or date picker controls. The default value for each parameter can be set based on the user name (QuickSight calls this a dynamic default). You could, for example, set an appropriate default based on each user’s office location, department, or sales territory. Here’s an example:

To learn more, read about Parameters in QuickSight.

URL Actions for Linked Dashboards
You can now connect your QuickSight dashboards to external applications by defining URL actions on visuals. The actions can include parameters, and become available in the Details menu for the visual. URL actions are defined like this:

You can use this feature to link QuickSight dashboards to third party applications (e.g. Salesforce) or to your own internal applications. Read Custom URL Actions to learn how to use this feature.

Dashboard Sharing
You can now share QuickSight dashboards across every user in an account.

Larger SPICE Tables
The per-data set limit for SPICE tables has been raised from 10 GB to 25 GB.

Upgrade to Enterprise Edition
The QuickSight administrator can now upgrade an account from Standard Edition to Enterprise Edition with a click. This enables provisioning of Readers with pay-per-session pricing, private VPC access, row-level security for dashboards and data sets, and hourly refresh of data sets. Enterprise Edition pricing applies after the upgrade.

Available Now
Everything I listed above is available now and you can start using it today!

You can try QuickSight for 60 days at no charge, and you can also attend our June 20th Webinar.

Jeff;

 

Amazon Neptune Generally Available

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/amazon-neptune-generally-available/

Amazon Neptune is now Generally Available in US East (N. Virginia), US East (Ohio), US West (Oregon), and EU (Ireland). Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. At the core of Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with millisecond latencies. Neptune supports two popular graph models, Property Graph and RDF, through Apache TinkerPop Gremlin and SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets. Neptune can be used to power everything from recommendation engines and knowledge graphs to drug discovery and network security. Neptune is fully-managed with automatic minor version upgrades, backups, encryption, and fail-over. I wrote about Neptune in detail for AWS re:Invent last year and customers have been using the preview and providing great feedback that the team has used to prepare the service for GA.

Now that Amazon Neptune is generally available there are a few changes from the preview:

Launching an Amazon Neptune Cluster

Launching a Neptune cluster is as easy as navigating to the AWS Management Console and clicking create cluster. Of course you can also launch with CloudFormation, the CLI, or the SDKs.

You can monitor your cluster health and the health of individual instances through Amazon CloudWatch and the console.

Additional Resources

We’ve created two repos with some additional tools and examples here. You can expect continuous development on these repos as we add additional tools and examples.

  • Amazon Neptune Tools Repo
    This repo has a useful tool for converting GraphML files into Neptune compatible CSVs for bulk loading from S3.
  • Amazon Neptune Samples Repo
    This repo has a really cool example of building a collaborative filtering recommendation engine for video game preferences.

Purpose Built Databases

There’s an industry trend where we’re moving more and more onto purpose-built databases. Developers and businesses want to access their data in the format that makes the most sense for their applications. As cloud resources make transforming large datasets easier with tools like AWS Glue, we have a lot more options than we used to for accessing our data. With tools like Amazon Redshift, Amazon Athena, Amazon Aurora, Amazon DynamoDB, and more we get to choose the best database for the job or even enable entirely new use-cases. Amazon Neptune is perfect for workloads where the data is highly connected across data rich edges.

I’m really excited about graph databases and I see a huge number of applications. Looking for ideas of cool things to build? I’d love to build a web crawler in AWS Lambda that uses Neptune as the backing store. You could further enrich it by running Amazon Comprehend or Amazon Rekognition on the text and images found and creating a search engine on top of Neptune.

As always, feel free to reach out in the comments or on twitter to provide any feedback!

Randall

Measuring the throughput for Amazon MQ using the JMS Benchmark

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/measuring-the-throughput-for-amazon-mq-using-the-jms-benchmark/

This post is courtesy of Alan Protasio, Software Development Engineer, Amazon Web Services

Just like compute and storage, messaging is a fundamental building block of enterprise applications. Message brokers (aka “message-oriented middleware”) enable different software systems, often written in different languages, on different platforms, running in different locations, to communicate and exchange information. Mission-critical applications, such as CRM and ERP, rely on message brokers to work.

A common performance consideration for customers deploying a message broker in a production environment is the throughput of the system, measured as messages per second. This is important to know so that application environments (hosts, threads, memory, etc.) can be configured correctly.

In this post, we demonstrate how to measure the throughput for Amazon MQ, a new managed message broker service for ActiveMQ, using JMS Benchmark. It should take between 15–20 minutes to set up the environment and an hour to run the benchmark. We also provide some tips on how to configure Amazon MQ for optimal throughput.

Benchmarking throughput for Amazon MQ

ActiveMQ can be used for a number of use cases. These use cases can range from simple fire and forget tasks (that is, asynchronous processing), low-latency request-reply patterns, to buffering requests before they are persisted to a database.

The throughput of Amazon MQ is largely dependent on the use case. For example, if you have non-critical workloads such as gathering click events for a non-business-critical portal, you can use ActiveMQ in a non-persistent mode and get extremely high throughput with Amazon MQ.

On the flip side, if you have a critical workload where durability is extremely important (meaning that you can’t lose a message), then you are bound by the I/O capacity of your underlying persistence store. We recommend using mq.m4.large for the best results. The mq.t2.micro instance type is intended for product evaluation. Performance is limited, due to the lower memory and burstable CPU performance.

Tip: To improve your throughput with Amazon MQ, make sure that you have consumers processing messaging as fast as (or faster than) your producers are pushing messages.

Because it’s impossible to talk about how the broker (ActiveMQ) behaves for each and every use case, we walk through how to set up your own benchmark for Amazon MQ using our favorite open-source benchmarking tool: JMS Benchmark. We are fans of the JMS Benchmark suite because it’s easy to set up and deploy, and comes with a built-in visualizer of the results.

Non-Persistent Scenarios – Queue latency as you scale producer throughput

JMS Benchmark nonpersistent scenarios

Getting started

At the time of publication, you can create an mq.m4.large single-instance broker for testing for $0.30 per hour (US pricing).

This walkthrough covers the following tasks:

  1.  Create and configure the broker.
  2. Create an EC2 instance to run your benchmark
  3. Configure the security groups
  4.  Run the benchmark.

Step 1 – Create and configure the broker
Create and configure the broker using Tutorial: Creating and Configuring an Amazon MQ Broker.

Step 2 – Create an EC2 instance to run your benchmark
Launch the EC2 instance using Step 1: Launch an Instance. We recommend choosing the m5.large instance type.

Step 3 – Configure the security groups
Make sure that all the security groups are correctly configured to let the traffic flow between the EC2 instance and your broker.

  1. Sign in to the Amazon MQ console.
  2. From the broker list, choose the name of your broker (for example, MyBroker)
  3. In the Details section, under Security and network, choose the name of your security group or choose the expand icon ( ).
  4. From the security group list, choose your security group.
  5. At the bottom of the page, choose Inbound, Edit.
  6. In the Edit inbound rules dialog box, add a role to allow traffic between your instance and the broker:
    • Choose Add Rule.
    • For Type, choose Custom TCP.
    • For Port Range, type the ActiveMQ SSL port (61617).
    • For Source, leave Custom selected and then type the security group of your EC2 instance.
    • Choose Save.

Your broker can now accept the connection from your EC2 instance.

Step 4 – Run the benchmark
Connect to your EC2 instance using SSH and run the following commands:

$ cd ~
$ curl -L https://github.com/alanprot/jms-benchmark/archive/master.zip -o master.zip
$ unzip master.zip
$ cd jms-benchmark-master
$ chmod a+x bin/*
$ env \
  SERVER_SETUP=false \
  SERVER_ADDRESS={activemq-endpoint} \
  ACTIVEMQ_TRANSPORT=ssl\
  ACTIVEMQ_PORT=61617 \
  ACTIVEMQ_USERNAME={activemq-user} \
  ACTIVEMQ_PASSWORD={activemq-password} \
  ./bin/benchmark-activemq

After the benchmark finishes, you can find the results in the ~/reports directory. As you may notice, the performance of ActiveMQ varies based on the number of consumers, producers, destinations, and message size.

Amazon MQ architecture

The last bit that’s important to know so that you can better understand the results of the benchmark is how Amazon MQ is architected.

Amazon MQ is architected to be highly available (HA) and durable. For HA, we recommend using the multi-AZ option. After a message is sent to Amazon MQ in persistent mode, the message is written to the highly durable message store that replicates the data across multiple nodes in multiple Availability Zones. Because of this replication, for some use cases you may see a reduction in throughput as you migrate to Amazon MQ. Customers have told us they appreciate the benefits of message replication as it helps protect durability even in the face of the loss of an Availability Zone.

Conclusion

We hope this gives you an idea of how Amazon MQ performs. We encourage you to run tests to simulate your own use cases.

To learn more, see the Amazon MQ website. You can try Amazon MQ for free with the AWS Free Tier, which includes up to 750 hours of a single-instance mq.t2.micro broker and up to 1 GB of storage per month for one year.

Pirate IPTV Service Gave Customer Details to Premier League, But What’s the Risk?

Post Syndicated from Andy original https://torrentfreak.com/pirate-iptv-service-gave-customer-details-to-premier-league-but-whats-the-risk-180515/

In a report last weekend, we documented what appear to be the final days of pirate IPTV provider Ace Hosting.

From information provided by several sources including official liquidation documents, it became clear that a previously successful and profitable Ace had succumbed to pressure from the Premier League, which accused the service of copyright infringement.

The company had considerable funds in the bank – £255,472.00 to be exact – but it also had debts of £717,278.84, including £260,000 owed to HMRC and £100,000 to the Premier League as part of a settlement agreement.

Information received by TF late Sunday suggested that £100K was the tip of the iceberg as far as the Premier League was concerned and in a statement yesterday, the football outfit confirmed that was the case.

“A renowned pirate of Premier League content to consumers has been forced to liquidate after agreeing to pay £600,000 for breaching the League’s copyright,” the Premier League announced.

“Ace IPTV, run by Craig Driscoll and Ian Isaac, was selling subscriptions to illegal Premier League streams directly to consumers which allowed viewing on a range of devices, including notorious Kodi-type boxes, as well as to smaller resellers in the UK and abroad.”

Sources familiar with the case suggest that while Ace Hosting Limited didn’t have the funds to pay the Premier League the full £600K, Ace’s operators agreed to pay (and have already paid, to some extent at least) what were essentially their own funds to cover amounts above the final £100K, which is due to be paid next year.

But that’s not the only thing that’s been handed over to the Premier League.

“Ace voluntarily disclosed the personal details of their customers, which the League will now review in compliance with data protection legislation. Further investigations will be conducted, and action taken where appropriate,” the Premier League added.

So, the big question now is how exposed Ace’s former subscribers are.

The truth is that only the Premier League knows for sure but TF has been able to obtain information from several sources which indicate that former subscribers probably aren’t the Premier League’s key interest and even if they were, information obtained on them would be of limited use.

According to a source with knowledge of how a system like Ace’s works, there is a separation of data which appears to help (at least to some degree) with the subscriber’s privacy.

“The system used to manage accounts and take payment is actually completely separate from the software used to manage streams and the lines themselves. They are never usually even on the same server so are two very different databases,” he told TF.

“So at best the only information that has voluntarily been provided to the [Premier League], is just your email, name and address (assuming you even used real details) and what hosting package or credits you bought.”

While this information is bad enough, the action against Ace is targeted, in that it focuses on the Premier League’s content and how Ace (and therefore its users) infringed on the football outfit’s copyrights. So, proving that subscribers actually watched any Premier League content would be an ideal position but it’s not straightforward, despite the potential for detailed logging.

“The management system contains no history of what you watched, when you watched it, when you signed in and so on. That is all contained in a different database on a different server.

“Because every connection is recorded [on the second server], it can create some two million entries a day and as such most providers either turn off this feature or delete the logs daily as having so many entries slows down the system down used for actual streams,” he explains.

Our source says that this data would likely to have been the first to be deleted and is probably “long gone” by now. However, even if the Premier League had obtained it, it’s unlikely they would be able to do much with it due to data protection laws.

“The information was passed to the [Premier League] voluntarily by ACE which means this information has been given from one entity to another without the end users’ consent, not part of the [creditors’ voluntary liquidation] and without a court order to support it. Data Protection right now is taken very seriously in the EU,” he notes.

At this point, it’s probably worth noting that while the word “voluntarily” has been used several times to explain the manner in which Ace handed over its subscribers’ details to the Premier League, the same word can be used to describe the manner in which the £600K settlement amount will be paid.

No one forces someone to pay or hand something over, that’s what the courts are for, and the aim here was to avoid that eventuality.

Other pieces of information culled from various sources suggest that PayPal payment information, limited to amounts only, was also handed over to the Premier League. And, perhaps most importantly (and perhaps predictably) as far as former subscribers are concerned, the football group was more interested in Ace’s upwards supplier chain (the ‘wholesale’ stream suppliers used, for example) than those buying the service.

Finally, while the Premier League is now seeking to send a message to customers that these services are risky to use, it’s difficult to argue with the assertion that it’s unsafe to hand over personal details to an illegal service.

“Ace IPTV’s collapse also highlighted the risk consumers take with their personal data when they sign up to illegal streaming services,” Premier League notes.

TF spoke with three IPTV providers who all confirmed that they don’t care what names and addresses people use to sign up with and that no checks are carried out to make sure they’re correct. However, one concedes that in order to run as a business, this information has to be requested and once a customer types it in, it’s possible that it could be handed over as part of a settlement.

“I’m not going to tell people to put in dummy details, how can I? It’s up to people to use their common sense. If they’re still worried they should give Sky their money because if our backs are against the wall, what do you think is going to happen?” he concludes.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN reviews, discounts, offers and coupons.

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.

 

 

 

Amazon Aurora Backtrack – Turn Back Time

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/amazon-aurora-backtrack-turn-back-time/

We’ve all been there! You need to make a quick, seemingly simple fix to an important production database. You compose the query, give it a once-over, and let it run. Seconds later you realize that you forgot the WHERE clause, dropped the wrong table, or made another serious mistake, and interrupt the query, but the damage has been done. You take a deep breath, whistle through your teeth, wish that reality came with an Undo option. Now what?

New Amazon Aurora Backtrack
Today I would like to tell you about the new backtrack feature for Amazon Aurora. This is as close as we can come, given present-day technology, to an Undo option for reality.

This feature can be enabled at launch time for all newly-launched Aurora database clusters. To enable it, you simply specify how far back in time you might want to rewind, and use the database as usual (this is on the Configure advanced settings page):

Aurora uses a distributed, log-structured storage system (read Design Considerations for High Throughput Cloud-Native Relational Databases to learn a lot more); each change to your database generates a new log record, identified by a Log Sequence Number (LSN). Enabling the backtrack feature provisions a FIFO buffer in the cluster for storage of LSNs. This allows for quick access and recovery times measured in seconds.

After that regrettable moment when all seems lost, you simply pause your application, open up the Aurora Console, select the cluster, and click Backtrack DB cluster:

Then you select Backtrack and choose the point in time just before your epic fail, and click Backtrack DB cluster:

Then you wait for the rewind to take place, unpause your application and proceed as if nothing had happened. When you initiate a backtrack, Aurora will pause the database, close any open connections, drop uncommitted writes, and wait for the backtrack to complete. Then it will resume normal operation and being to accept requests. The instance state will be backtracking while the rewind is underway:

The console will let you know when the backtrack is complete:

If it turns out that you went back a bit too far, you can backtrack to a later time. Other Aurora features such as cloning, backups, and restores continue to work on an instance that has been configured for backtrack.

I’m sure you can think of some creative and non-obvious use cases for this cool new feature. For example, you could use it to restore a test database after running a test that makes changes to the database. You can initiate the restoration from the API or the CLI, making it easy to integrate into your existing test framework.

Things to Know
This option applies to newly created MySQL-compatible Aurora database clusters and to MySQL-compatible clusters that have been restored from a backup. You must opt-in when you create or restore a cluster; you cannot enable it for a running cluster.

This feature is available now in all AWS Regions where Amazon Aurora runs, and you can start using it today.

Jeff;

AWS Online Tech Talks – May and Early June 2018

Post Syndicated from Devin Watson original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-may-and-early-june-2018/

AWS Online Tech Talks – May and Early June 2018  

Join us this month to learn about some of the exciting new services and solution best practices at AWS. We also have our first re:Invent 2018 webinar series, “How to re:Invent”. Sign up now to learn more, we look forward to seeing you.

Note – All sessions are free and in Pacific Time.

Tech talks featured this month:

Analytics & Big Data

May 21, 2018 | 11:00 AM – 11:45 AM PT Integrating Amazon Elasticsearch with your DevOps Tooling – Learn how you can easily integrate Amazon Elasticsearch Service into your DevOps tooling and gain valuable insight from your log data.

May 23, 2018 | 11:00 AM – 11:45 AM PTData Warehousing and Data Lake Analytics, Together – Learn how to query data across your data warehouse and data lake without moving data.

May 24, 2018 | 11:00 AM – 11:45 AM PTData Transformation Patterns in AWS – Discover how to perform common data transformations on the AWS Data Lake.

Compute

May 29, 2018 | 01:00 PM – 01:45 PM PT – Creating and Managing a WordPress Website with Amazon Lightsail – Learn about Amazon Lightsail and how you can create, run and manage your WordPress websites with Amazon’s simple compute platform.

May 30, 2018 | 01:00 PM – 01:45 PM PTAccelerating Life Sciences with HPC on AWS – Learn how you can accelerate your Life Sciences research workloads by harnessing the power of high performance computing on AWS.

Containers

May 24, 2018 | 01:00 PM – 01:45 PM PT – Building Microservices with the 12 Factor App Pattern on AWS – Learn best practices for building containerized microservices on AWS, and how traditional software design patterns evolve in the context of containers.

Databases

May 21, 2018 | 01:00 PM – 01:45 PM PTHow to Migrate from Cassandra to Amazon DynamoDB – Get the benefits, best practices and guides on how to migrate your Cassandra databases to Amazon DynamoDB.

May 23, 2018 | 01:00 PM – 01:45 PM PT5 Hacks for Optimizing MySQL in the Cloud – Learn how to optimize your MySQL databases for high availability, performance, and disaster resilience using RDS.

DevOps

May 23, 2018 | 09:00 AM – 09:45 AM PT.NET Serverless Development on AWS – Learn how to build a modern serverless application in .NET Core 2.0.

Enterprise & Hybrid

May 22, 2018 | 11:00 AM – 11:45 AM PTHybrid Cloud Customer Use Cases on AWS – Learn how customers are leveraging AWS hybrid cloud capabilities to easily extend their datacenter capacity, deliver new services and applications, and ensure business continuity and disaster recovery.

IoT

May 31, 2018 | 11:00 AM – 11:45 AM PTUsing AWS IoT for Industrial Applications – Discover how you can quickly onboard your fleet of connected devices, keep them secure, and build predictive analytics with AWS IoT.

Machine Learning

May 22, 2018 | 09:00 AM – 09:45 AM PTUsing Apache Spark with Amazon SageMaker – Discover how to use Apache Spark with Amazon SageMaker for training jobs and application integration.

May 24, 2018 | 09:00 AM – 09:45 AM PTIntroducing AWS DeepLens – Learn how AWS DeepLens provides a new way for developers to learn machine learning by pairing the physical device with a broad set of tutorials, examples, source code, and integration with familiar AWS services.

Management Tools

May 21, 2018 | 09:00 AM – 09:45 AM PTGaining Better Observability of Your VMs with Amazon CloudWatch – Learn how CloudWatch Agent makes it easy for customers like Rackspace to monitor their VMs.

Mobile

May 29, 2018 | 11:00 AM – 11:45 AM PT – Deep Dive on Amazon Pinpoint Segmentation and Endpoint Management – See how segmentation and endpoint management with Amazon Pinpoint can help you target the right audience.

Networking

May 31, 2018 | 09:00 AM – 09:45 AM PTMaking Private Connectivity the New Norm via AWS PrivateLink – See how PrivateLink enables service owners to offer private endpoints to customers outside their company.

Security, Identity, & Compliance

May 30, 2018 | 09:00 AM – 09:45 AM PT – Introducing AWS Certificate Manager Private Certificate Authority (CA) – Learn how AWS Certificate Manager (ACM) Private Certificate Authority (CA), a managed private CA service, helps you easily and securely manage the lifecycle of your private certificates.

June 1, 2018 | 09:00 AM – 09:45 AM PTIntroducing AWS Firewall Manager – Centrally configure and manage AWS WAF rules across your accounts and applications.

Serverless

May 22, 2018 | 01:00 PM – 01:45 PM PTBuilding API-Driven Microservices with Amazon API Gateway – Learn how to build a secure, scalable API for your application in our tech talk about API-driven microservices.

Storage

May 30, 2018 | 11:00 AM – 11:45 AM PTAccelerate Productivity by Computing at the Edge – Learn how AWS Snowball Edge support for compute instances helps accelerate data transfers, execute custom applications, and reduce overall storage costs.

June 1, 2018 | 11:00 AM – 11:45 AM PTLearn to Build a Cloud-Scale Website Powered by Amazon EFS – Technical deep dive where you’ll learn tips and tricks for integrating WordPress, Drupal and Magento with Amazon EFS.