Data and AI releases

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Amazon EMR introduces Long Term Support with Apache Spark 4.1

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Amazon EMR introduces Long Term Support (LTS) releases, starting with emr-spark-8.1.0 and Apache Spark 4.1. With LTS, designated versions of the AWS runtime for Apache Spark receive 36 months of support. Amazon EMR provides LTS releases with fixes for critical and high severity security, bug, and data-corruption issues, subject to availability.

SQLGovernanceSecurityPricingApache Iceberg

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Also shipped Sep 22, 2026AWS in September 2026 →

Shopify connector (Beta)

The Shopify connector is now available in Lakeflow Connect in Beta . Workspace admins can control access to this feature from the Previews page. The connector ingests e-commerce data such as products, orders, customers, inventory, fulfillments, and discounts from Shopify into Databricks. Authentication uses the Shopify OAuth client credentials grant.

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Lakebase backup schedule API (Beta)

You can now manage Lakebase backup schedules programmatically. The snapshot schedule API lets you read and set a root branch's automated snapshot cadences (daily, weekly, or monthly) and how long each cadence's snapshots are retained. This API is in Beta and available to all Lakebase workspaces. See Create a backup schedule .

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More from Amazon Athena and EMREvery Amazon Athena and EMR release →

Amazon EMR 7.14 is now available

Amazon EMR 7.14 is now available with new features across Amazon EMR on EC2, Amazon EMR on EKS, and Amazon EMR Serverless, along with version upgrades for additional applications. This release also upgrades Apache Spark to 3.5.8 and Apache Iceberg to 1.10.1.

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Amazon EMR on EKS now supports IPv6 Amazon EKS clusters

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.

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Serverless Storage on Amazon EMR Serverless now supports terabyte-scale shuffle

Amazon EMR Serverless now offers enhanced serverless storage capabilities with support for up to 1TB shuffle operations, raising the previous 200 GB per-job limit. Amazon EMR Serverless makes it simple for data engineers and data scientists to run open-source big data analytics frameworks without configuring, managing, and scaling clusters or servers.

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Amazon EMR Serverless now supports IPv6 and dual-stack connectivity

Amazon EMR Serverless now supports IPv6 and dual-stack connectivity, expanding beyond the IPv4-only connectivity that was previously available. You can now run your Apache Spark and Apache Hive jobs over IPv6 in your Amazon Virtual Private Cloud (VPC) by supplying dual-stack (IPv4 and IPv6) or IPv6-only subnets.

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