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Amazon Bedrock Managed Knowledge Base now supports Salesforce and Zendesk as native data source connectors

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AWS announces Salesforce and Zendesk data source connectors for Amazon Bedrock Managed Knowledge Base, a fully managed retrieval-augmented generation (RAG) service. Customers can now sync Salesforce knowledge articles and Zendesk articles and community posts directly into their managed knowledge base.

Previously, bringing content from these platforms into Bedrock Knowledge Bases required building custom ingestion pipelines—now, you provide your instance credentials, and the connectors handle data crawling, metadata extraction, and incremental sync automatically.

These connectors make it easy to build AI agents and assistants grounded in the support and product knowledge your teams already maintain in Salesforce and Zendesk. For example, power a customer-facing support bot with up-to-date Zendesk help center articles and community answers, or build an internal sales enablement assistant that retrieves relevant Salesforce knowledge articles during deal preparation. By keeping your knowledge base in sync with these platforms, your retrieval-augmented generation applications always reflect the latest content without manual intervention.

To learn more, see Salesforce data source connector and Zendesk data source connector in the Amazon Bedrock User Guide. For more information about Amazon Bedrock Managed Knowledge Base, visit the Amazon Bedrock Knowledge Bases product page .

Amazon Connect Customer now provides routing step data in the analytics data lake

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Amazon Connect Customer now provides routing step data in the analytics data lake, making it easier for customers to generate insights from routing decisions. Using the data lake, customers can leverage Amazon Athena and Amazon Quick to analyze trends, such as contacts queued and contacts joined at each routing step, without complex data pipelines. For example, an analyst can now report on how contacts progressed through each routing step, identify where agent matching criteria were relaxed, and measure the impact of routing step configurations on wait times and agent utilization.

Routing step data is available in all AWS regions where Amazon Connect Customer data lake is offered. To learn more about Amazon Connect Customer data lake, see the Amazon Connect Customer Administrator Guide .  To learn more about Amazon Connect Customer, visit the Amazon Connect Customer website . 

Amazon EMR on EKS now supports IPv6 Amazon EKS clusters

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Today, AWS announces that Amazon EMR on EKS now supports running workloads on IPv6 Amazon Elastic Kubernetes Service (Amazon EKS) clusters. Amazon EMR on EKS enables data platform and analytics teams to run open-source big data frameworks such as Apache Spark and Apache Flink on Amazon EKS. With IPv6 support, teams operating at scale can now run these workloads on IPv6 Amazon EKS clusters, giving them access to the vastly larger address space of IPv6 as their analytics workloads grow.

You can now scale large Spark and Flink workloads using the expanded IPv6 address space, which removes the need for IPv4 conservation workarounds such as secondary CIDR ranges or prefix delegation. High-executor jobs, such as 500-executor Spark jobs, can run concurrently without planning around address limits. To submit workloads, use StartJobRun, Spark Connect Interactive Endpoints, and Amazon SageMaker Unified Studio with no additional configuration, while the Flink, Livy, and Spark Operators are also supported. This capability is available at no additional cost, starting with Amazon EMR releases emr-7.14.0 and emr-spark-8.0.0.

IPv6 cluster support is available in all AWS Regions where Amazon EMR on EKS and IPv6 Amazon EKS clusters are available.

Amazon EMR on EKS IPv6 cluster support includes detailed setup instructions and documentation on supported submission models. Visit the Amazon EMR on EKS product page and the IPv6 documentation to learn more. 

Amazon Kinesis Data Streams announces Service-Managed Partition Keys for simplified data ingestion

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Amazon Kinesis Data Streams now supports service-managed partition keys for On-Demand Standard and On-Demand Advantage streams, automatically distributing records across shards without requiring customers to specify partition keys to publish data. This capability simplifies data ingestion for workloads where record ordering is not required, eliminating hot partition keys and reducing time to production for streaming workloads.

Amazon Kinesis Data Streams is a serverless streaming data service that makes it easy to capture, process, and store data streams at any scale. Many streaming use cases such as log aggregation, metrics collection, and IoT telemetry do not require ordering guarantees and benefit from prewarmed capacity for instant scaling. Previously, customers generated random partition keys (such as UUIDs) to distribute data, but random partitioning can still produce uneven throughput across shards, causing throttling for some partition keys even when the stream has sufficient aggregate capacity. By opting into service-managed partition keys, customers no longer need to specify partition keys when publishing data to streams in on-demand mode. The service automatically distributes records based on available warm capacity, allowing customers to scale to gigabytes per second without maintaining any distribution logic. Customers who want to send records without specifying a partition key can simply upgrade to the latest AWS SDK or Kinesis Producer Library (KPL) version to benefit from this capability.

Service-managed partition keys for Amazon Kinesis Data Streams is available today in all AWS commercial regions at no additional cost. To get started, visit the Amazon Kinesis Data Streams documentation (https://aws.amazon.com/kinesis ).

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