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Amazon API Gateway boosts compression, tagging

AWS customers were enticed by products and services introduced at the cloud provider’s annual customer and partner confab, re:Invent, held recently. AWS also kept up a steady pace of basic service updates to round out 2017, which included some API management capabilities.

Amazon API Gateway now offers content encoding support, which lets a client  compress content before a response to an API request. This feature can cut costs and improve performance, as it reduces the amount of data sent from the service to clients. Developers can define the minimum response size and enable encoding in the API itself.

The service also lets developers use application logic in custom Lambda authorizer functions to support API keys. This makes it simpler to control usage assigned to API requests, and the feature also allows teams to track request properties to API keys, such as HTTP request headers.

Additionally, Amazon API Gateway lets teams tag API stages for better organization of resources. Teams can filter API stage allocation tags through AWS Budgets to potentially reduce costs. The API Gateway feature also helps categorize APIs.

Catch up on re:Invent

AWS released several products and features at its AWS annual re:Invent conference that were not called out in this blog. Catch up on what you missed with oodles of re:Invent news and analysis from our team of writers.

New features and support

  • Restart logic in ECS. The Amazon Elastic Container Service (ECS) scheduler lets a developer program logic to control retry attempts for failing tasks. This feature reduces the potential cost and performance impacts of continuous attempts to run tasks that fail. The schedule can increase time between restart attempts, stop the deployment and add a message to notify developers.
  • Speed up Redshift queries. AWS’ data warehouse, Amazon Redshift, added late materialization with row-level filters to improve performance by reducing the amount of data it scans. Predicate filters reduce scans to only table items that satisfy criteria to boost query performance by attrition. AWS enables this feature by default.
  • Customize edge error responses. Lambda@Edge now lets developers respond with Lambda functions when CloudFront receives an error from your origin. Developers can access and define responses for 4XX and 5XX error status codes, and they can add headers, redirects and dynamically issue responses to end users based on their requests.
  • Send real-time SQL data to Lambda. Developers can configure Amazon Kinesis Data Analytics to output real-time data to AWS Lambda. From there, they can code functions that respond to that SQL data, such as send an alert or update a database.
  • Cross-account S3 bucket access from QuickSight. Data analysts can now use a QuickSight account tied to a specific AWS account to access data stored in Simple Storage Service (S3) buckets that belong to another AWS account. This cross-account S3 access enables more seamless data analysis for large businesses with multiple departments.
  • More instance support for PostgreSQL databases. Amazon Relational Database Service (RDS) for PostgreSQL added support for R4, db.t2.xlarge, db.t2.2xlarge, and db.m4.16xlarge instances for enhanced performance.
  • Increase ES scale, decrease cost. Amazon Elasticsearch Service (ES) added support for I3 instances, which improve upon the previous generation of I/O-intensive instances. With I3 instances, developers can use up to 1.5 PB of storage in an ES cluster, 15 TB of data in each node, 3.3 million IOPS and 16 GB/s of sequential disk throughput – all for less than half the cost of I2 instances.
  • A NICE combination. After acquiring NICE in 2016, AWS combined with the Italian software company to release Desktop Cloud Visualization (DCV) 2017, a steaming and remote access service. DCV 2017 improves on-premises capabilities, and the service is now available on EC2 instances, such as Elastic GPU. AWS customers only pay for the underlying compute resources.
  • CloudFront enhances encryption. AWS’ content delivery network, Amazon CloudFront, introduced field-level encryption to protect sensitive data with HTTPS. This feature can be helpful for financial or personally identifiable information, ensuring that only specific components or services in a stack can decrypt and view that data.
  • Use containers in CD pipelines. Amazon CodePipeline added integration with container-based deployments to Amazon Elastic Container Service and AWS Fargate. Developers push code changes through a continuous delivery pipeline, which calls the desired service to create a container image, test and then update containers in production.
  • Process MySQL queries faster. Amazon Aurora sped up query processing with support for hash joins and batched scans. These features are available for Amazon Aurora MySQL version 1.16.
  • CloudWatch adds new visuals, encryption support. Amazon CloudWatch added two new chart visuals: zoom, for magnification of a shorter time period, and pan, for browsing a specific time interval. Administrators can find these visualization options in the CloudWatch Metrics console and dashboards. CloudWatch Logs also added support for integration with AWS Key Management Service (KMS), which enable an admin to encrypt logs with AWS-managed keys, if they choose.
  • KMS integrates with ES. Developers can now encrypt data at rest in Amazon ES with keys managed through KMS. This feature lets data scientists use ES while encrypting all data on the underlying file systems without application modification.
  • Set alerts for free tier usage. AWS Budgets include the capability to track service usage and send an email alert to administrators if it forecasts usage to exceed a free tier limit.
  • Define an IoT backup plan. Developers can now define a backup action in Amazon IoT Rules Engine if a primary action fails. In addition to keeping an application running, this feature preserves error message data, which can include unavailability of services and insufficient resource provisioning.

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