AWS Big Data Blog

Get started with Amazon DynamoDB zero-ETL integration with Amazon Redshift

We’re excited to announce the general availability (GA) of Amazon DynamoDB zero-ETL integration with Amazon Redshift, which enables you to run high-performance analytics on your DynamoDB data in Amazon Redshift with little to no impact on production workloads running on DynamoDB. As data is written into a DynamoDB table, it’s seamlessly made available in Amazon Redshift, eliminating the need to build and maintain complex data pipelines.

Elevate your search and analytics skills with the new Amazon OpenSearch Service YouTube channel

We’re thrilled to announce the launch of the official Amazon OpenSearch Service YouTube channel—a comprehensive resource for anyone looking to master Amazon OpenSearch Service. Whether you’re just getting started with searches , vectors, analytics, or you’re looking to optimize large-scale implementations, our channel can be your go-to resource to help you unlock the full potential of OpenSearch Service.

Migrate from Amazon Kinesis Data Analytics for SQL to Amazon Managed Service for Apache Flink and Amazon Managed Service for Apache Flink Studio

Amazon Kinesis Data Analytics for SQL is a data stream processing engine that helps you run your own SQL code against streaming sources to perform time series analytics, feed real-time dashboards, and create real-time metrics. AWS has made the decision to discontinue Kinesis Data Analytics for SQL, effective January 27, 2026. In this post, we explain why we plan to end support for Kinesis Data Analytics for SQL, alternative AWS offerings, and how to migrate your SQL queries and workloads.

Enhance Amazon EMR scaling capabilities with Application Master Placement

Starting with the Amazon EMR 7.2 release, Amazon EMR on EC2 introduced a new feature called Application Master (AM) label awareness, which allows users to enable YARN node labels to allocate the AM containers within On-Demand nodes only. In this post, we explore the key features and use cases where this new functionality can provide significant benefits, enabling cluster administrators to achieve optimal resource utilization, improved application reliability, and cost-efficiency in your EMR on EC2 clusters.

Take manual snapshots and restore in a different domain spanning across various Regions and accounts in Amazon OpenSearch Service

This post provides a detailed walkthrough about how to efficiently capture and manage manual snapshots in OpenSearch Service. It covers the essential steps for taking snapshots of your data, implementing safe transfer across different AWS Regions and accounts, and restoring them in a new domain. This guide is designed to help you maintain data integrity and continuity while navigating complex multi-Region and multi-account environments in OpenSearch Service.

Unleash deeper insights with Amazon Redshift data sharing for data lake tables

Amazon Redshift now enables the secure sharing of data lake tables—also known as external tables or Amazon Redshift Spectrum tables—that are managed in the AWS Glue Data Catalog, as well as Redshift views referencing those data lake tables. By using granular access controls, data sharing in Amazon Redshift helps data owners maintain tight governance over who can access the shared information. In this post, we explore powerful use cases that demonstrate how you can enhance cross-team and cross-organizational collaboration, reduce overhead, and unlock new insights by using this innovative data sharing functionality.

Amazon EMR on EC2 cost optimization: How a global financial services provider reduced costs by 30%

In this post, we highlight key lessons learned while helping a global financial services provider migrate their Apache Hadoop clusters to AWS and best practices that helped reduce their Amazon EMR, Amazon Elastic Compute Cloud (Amazon EC2), and Amazon Simple Storage Service (Amazon S3) costs by over 30% per month.

Perform data parity at scale for data modernization programs using AWS Glue Data Quality

In this post, we show you how to use AWS Glue Data Quality, a feature of AWS Glue, to establish data parity during data modernization and migration programs with minimal configuration and infrastructure setup. AWS Glue Data Quality enables you to automatically measure and monitor the quality of your data in data repositories and AWS Glue ETL pipelines.