Category: Analytics

In this blog post, we show you how Amazon Web Services (AWS) is simplifying data integration with zero-ETL while realizing performance benefits and cost optimizations. As organizations gather data for analytics and AI, they are increasingly finding themselves caught in a complex web of extract, transform, and load (ETL) pipelines—the traditional backbone of data integration. […]

When migrating from Teradata BTEQ (Basic Teradata Query) to Amazon Redshift RSQL, following established best practices helps ensure maintainable, efficient, and reliable code. While the AWS Schema Conversion Tool (AWS SCT) automatically handles the basic conversion of BTEQ scripts to RSQL, it primarily focuses on SQL syntax translation and basic script conversion. However, to achieve […]

Amazon Redshift Serverless now supports 4 RPU configurations, helping you get started with a lower base capacity that runs scalable analytics workloads beginning at $1.50 per hour. In this post, we examine how this new sizing option makes Redshift Serverless accessible to smaller organizations while providing enterprises with cost-effective environments for development, testing, and variable workloads.

In this post we explain how Zeta built a more unified monitoring solution using Amazon OpenSearch Service that improved performance, reduced manual processes, and increased end-user satisfaction. Zeta has achieved over an 80% reduction in mean time to resolution (MTTR), with incident response times decreasing from 30+ minutes to under 5 minutes.

In this post, we share field-tested patterns for log ingestion that have helped organizations successfully implement logging at scale, while maintaining optimal performance and managing costs effectively. A well-designed log analytics solution can help support proactive management in a variety of use cases, including debugging production issues, monitoring application performance, or meeting compliance requirements.

Large-scale HBase deployments on Amazon EMR suffer from unpredictable garbage collection behavior that creates performance bottlenecks for business-critical applications. To solve this problem, Amazon EMR leverages Oracle’s generational ZGC technology from JDK 21 to deliver predictable, sub-millisecond pause times. This post shows you how to configure generational ZGC in Amazon EMR 7.10.0, apply performance tuning methods, and optimize HBase RegionServer garbage collection settings.

ATPCO is the backbone of modern airline retailing, helping airlines and third-party channels deliver the right offers to customers at the right time. ATPCO addressed data governance challenges using Amazon DataZone. SageMaker Unified Studio, built on the same architecture as Amazon DataZone, offers additional capabilities, so users can complete various tasks such as building data pipelines using AWS Glue and Amazon EMR, or conducting analyses using Amazon Athena and Amazon Redshift query editor across diverse datasets, all within a single, unified environment. In this post, we walk you through the challenges ATPCO addresses for their business using SageMaker Unified Studio.

By deploying Amazon EMR on AWS Local Zones, organizations can achieve single-digit millisecond latency data processing for applications while maintaining data residency compliance. This post demonstrates how to use AWS Local Zones to deploy EMR clusters closer to your users, enabling millisecond-level response times.

This post demonstrates how Amazon Athena CREATE TABLE AS SELECT (CTAS) simplifies the data transformation process through a practical example: migrating an existing Parquet dataset into Amazon S3 Tables.

In this post, we demonstrate how you can export the JMX metrics for Debezium connector when used with Amazon MSK Connect.