Amazon Elastic Kubernetes Service (EKS) now supports a configurable Kubernetes service IP address range. This enables customers with clusters running in a peered or direct connected network environment to ensure that their pods can communicate with external applications on networks outside the cluster.
Amazon Kinesis Data Analytics for Java is now Amazon Kinesis Data Analytics for Apache Flink
AWS has repositioned Amazon Kinesis Data Analytics for Java to Amazon Kinesis Data Analytics for Apache Flink to emphasize our continued support for Apache Flink.
Amazon AppFlow supports new options for updating records
Amazon AppFlow, a fully managed integration service that enables customers to securely transfer data between AWS services and cloud applications, now supports the option of upserting records in destinations, in addition to already supported option of inserting records. This feature is now available when Salesforce is selected as the destination. The upsert operation updates existing records in Salesforce if they exist; otherwise, it inserts the data as new records. Customers can now insert, or upsert up to 500 MB of data in Salesforce in a single flow run.
AWS and Grafana Labs launch AWS X-Ray data source plugin
Today, AWS and Grafana Labs launched an AWS X-Ray data source plugin. You can use the latest release of Grafana (version 7.2.0 or later) to help visualize your AWS X-Ray traces directly in your Grafana dashboards in order to triage performance issues.
Employee onboarding app template now available for Amazon Honeycode
You can now use an Amazon Honeycode app template that helps you build an automated employee onboarding process for your team or organization. Amazon Honeycode is a fully managed service that allows customers to quickly build powerful mobile and web applications for team productivity scenarios – with no programming required. The new template is intended for Honeycode builders who are seeking more app examples and want to study best practices for building apps.
Amazon AppFlow supports new options for schedule triggered flows
Amazon AppFlow, a fully managed integration service that enables customers to securely transfer data between AWS services and cloud applications, now allows you to select additional time-stamp fields as the criteria for determining incremental data in schedule triggered flows. In a given flow run, AppFlow supports the ability to transfer only records that have changed since the previous successful flow run. Previously, the criteria for determining if a record has changed was not configurable. Now, you can choose any suitable source field, such as created date and modified date, as the criteria for determining which records have changed.
Introducing the redesigned AWS Architecture Center
The redesigned AWS Architecture Center helps you find the information you need to design and operate reliable, secure, efficient, and cost-effective cloud applications, right from the start. The Architecture Center aggregates best practices, reference architecture deployments, reference architecture diagrams, and more, making it easier for you to discover what’s most important. The new Architecture Center also provides new ways for you to share feedback by voting on proposed guidance, requesting content, and more.
You now can design and visualize Amazon Keyspaces data models more easily by using NoSQL Workbench
You now can design and visualize Amazon Keyspaces (for Apache Cassandra) data models more easily by using NoSQL Workbench . NoSQL Workbench now provides you a point-and-click interface to create nonrelational data models for Amazon Keyspaces, a scalable, highly available, and managed Apache Cassandra–compatible database service.
Amazon Redshift announces support for HyperLogLog Sketches
Amazon Redshift introduces support for natively storing and processing HyperLogLog (HLL) sketches. HyperLogLog is a novel algorithm that efficiently estimates the approximate number of distinct values in a data set. HLL sketch is a construct that encapsulates the information about the distinct values in the data set. You can use HLL sketches to achieve significant performance benefits for queries that compute approximate cardinality over large data sets, with an average relative error between 0.01–0.6%.
AWS Transfer Family supports FIPS 140-2 compliant endpoints in AWS GovCloud (US) Regions
AWS Transfer Family now offers Federal Information Processing Standards (FIPS) 140-2 compliant endpoints in AWS GovCloud (US) Regions to protect sensitive information. These endpoints terminate Transport Layer Security (TLS) sessions using a FIPS 140-2 compliant cryptographic software module, making it easier for you to use Transfer Family for regulated workloads.