Category: Analytics

In Part 1 of this series, we showed how to simplify enterprise data access using the Amazon Redshift integration with Amazon S3 Access Grants. In this post, we extend that solution across AWS Regions. We introduce a fictional company, AnyCompany Global, to illustrate how organizations with global operations can use AWS IAM Identity Center Multi-Region to set up consistent, identity-based access to Amazon Redshift and Amazon S3 Tables across Regions.

In this post, we show you how to diagnose multi-layer Medallion Architecture pipeline failures in minutes using AWS DevOps Agent with Apache Spark Troubleshooting Agent integrated as an MCP server.

Amazon SageMaker Data Agent launches three new capabilities in Amazon SageMaker Unified Studio notebooks: SQL analytics on Snowflake data sources, materialized view management, and interactive charting. Practitioners can use them together to query Snowflake alongside AWS data, pre-compute and schedule repeated aggregations, and create interactive visualizations from natural language prompts in a single notebook, without writing boilerplate code or switching tools. In this post, we describe the challenges these capabilities address, introduce each one, and walk through a fraud analytics scenario that demonstrates them working together in an end-to-end investigation workflow.

In this post, we explore how Nexthink combined Amazon OpenSearch Service vector search, Amazon Bedrock, and infrastructure as code to power the Spark agent’s retrieval layer.

With the AWS Toolkit for Visual Studio Code, you can connect Kiro, VS Code, or Cursor directly to Amazon SageMaker Unified Studio. This post demonstrates the integration using Kiro. The same Remote Access connection works with VS Code and Cursor. The post starts by showing what you can do with this integration: using natural language to explore and analyze data in a governed environment. We then walk through the setup so you can try it yourself.

In this post, we demonstrate reading from and writing to Lake Formation-managed S3 locations using Apache Spark jobs from EMR. Lake Formation credential vending for S3 location access is available in EMR release label 7.13 and later, Boto3 1.42.29 and later, AWS Java SDK 2.41.32 and later, and AWS Command Line Interface (AWS CLI) version 2.33.1 and later.

In this post, we explore how to build an online shopping AI agent. We focus on its architecture and implementation with Amazon OpenSearch Service, Amazon Bedrock AgentCore, and Strands Agents. Amazon Bedrock AgentCore is an agentic platform for deploying and operating those agents and tools securely at scale without managing infrastructure.

In this post, we show you how to build a CDC pipeline that delivers query-ready Iceberg tables directly. The pipeline captures inserts, updates, and deletes from Aurora PostgreSQL and applies them as row-level operations in Amazon S3 Tables, a capability of Amazon Simple Storage Service (Amazon S3).

This post is part 1 of a two-part series. We walk through the basics: creating an Iceberg V3 table with a VARIANT column, inserting semi-structured data, and querying it with variant_get(). In Part 2, we scale to millions of rows and benchmark VARIANT against traditional string storage. We measure the difference in query performance and storage footprint.

In this post, we walk through a hands-on PySpark migration from Spark 3.5 to Spark 4.0 on Amazon EMR Serverless, using the AWS Spark Upgrade Agent. You’ll see how the agent iteratively validates your application on a live Amazon EMR Serverless application, automatically diagnosing and resolving failures from Amazon CloudWatch logs until the job succeeds.