Category: Analytics

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.

In this post, we demonstrate the critical role of metadata in text-to-SQL generation through an example implemented for Amazon Athena using Amazon Bedrock. We discuss the challenges in maintaining the metadata as well as ways to overcome those challenges and enrich the metadata.

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.

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.

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.

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.

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.

We recently announced a new capacity level of 30TB for time series data per account per AWS Region. The OpenSearch Serverless compute capacity for data ingestion and search/query is measured in OpenSearch Compute Units (OCUs), which are shared among various collections with the same AWS Key Management Service (AWS KMS) key. This post discusses how you can analyze 30TB time series datasets with OpenSearch Serverless.

This post demonstrates how to implement a dynamic rules engine using Amazon Managed Service for Apache Flink. Our implementation provides the ability to create dynamic rules that can be created and updated without the need to change or redeploy the underlying code or implementation of the rules engine itself. We discuss the architecture, the key services of the implementation, some implementation details that you can use to build your own rules engine, and an AWS Cloud Development Kit (AWS CDK) project to deploy this in your own account.

After careful consideration, we have made the decision to end support for Governed Tables, effective December 31, 2024, to focus on open source transactional table formats such as Apache Iceberg, Apache Hudi, and Linux Foundation Delta Lake. This decision stems from customer preference for these open source solutions, which offer ACID-compliant transactions, compaction, time travel, and other features previously provided by Governed Tables.