Category: Analytics

In this post, we walk you through a solution for implementing rack awareness in consumer applications that are dynamically deployed across multiple Availability Zones using Amazon EKS.

In this post, we explore its real-world impact through the lens of an ecommerce company striving to boost their bottom line. To illustrate this practical application, we walk you through how you can use the prebuilt integration between SageMaker Catalog and AWS Glue crawlers to automatically capture lineage for data assets stored in Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB.

This post explores how you can use AWS Glue Data Quality to maintain data quality of S3 Tables and Apache Iceberg tables on general purpose S3 buckets. We’ll discuss strategies for verifying the quality of published data and how these integrated technologies can be used to implement effective data quality workflows.

This post demonstrates how to build a solution by combining Amazon Simple Storage Service (Amazon S3) for data storage, AWS Glue Data Catalog for schema management, and Amazon Athena for one-time querying. We’ll focus specifically on handling Avro-formatted data in partitioned S3 buckets, where schemas can change frequently while providing consistent query capabilities across all data regardless of schema versions.

In this post, we discuss the design and security controls in place when using generative SQL and its use in both Amazon SageMaker Unified Studio and Amazon Redshift Query Editor v2.

In this post, we show how to use AWS service integrations to minimize custom code while providing a robust platform for industrial data ingestion, processing, and analytics. By using Amazon S3 Tables and its built-in optimizations, you can maximize query performance and minimize costs without additional infrastructure setup.

In this post, we demonstrate the powerful interoperability between Amazon EMR and Databricks Unity Catalog by walking through how to enable external access to Unity Catalog, configure EMR Spark to connect seamlessly with Unity Catalog, and perform DML and DDL operations on Unity Catalog tables using EMR Serverless.

Now, by using trusted identity propagation, IAM Identity Center provides a new, direct method for accessing data in OpenSearch Service. In this post, we outline how you can take advantage of this new access method to simplify data access using the OpenSearch UI and still maintain robust role-based access control for your OpenSearch data.

Customers new to Amazon OpenSearch Service often ask how many shards their indexes need. An index is a collection of shards, and an index’s shard count can affect both indexing and search request efficiency. OpenSearch Service can take in large amounts of data, split it into smaller units called shards, and distribute those shards across a dynamically changing set of instances. In this post, we provide some practical guidance for determining the ideal shard count for your use case.

Today, we’re announcing the open source release of Spark History Server MCP, a specialized Model Context Protocol (MCP) server that transforms this workflow by enabling AI assistants to access and analyze your existing Spark History Server data through natural language interactions. This project, developed collaboratively by AWS open source and Amazon SageMaker Data Processing, turns complex debugging sessions into conversational interactions that deliver faster, more accurate insights without requiring changes to your current Spark infrastructure. You can use this MCP server with your self-managed or AWS managed Spark History Servers to analyze Spark applications running in the cloud or on-premises deployments.