Category: AWS Glue

AWS Glue 6.0 is now available, lowering AWS Glue pricing by 30%, adding an AWS optimized build of Apache Spark 4.1, and introducing Apache Iceberg V3 capabilities suitable for enterprise adoption. This post covers the key capabilities and performance benefits, with code examples to help you get started.

Walk through upgrading a PySpark ETL job from AWS Glue 5.1 to AWS Glue 6.0 using the generative AI upgrades for Apache Spark. The upgrade analysis automatically detects incompatibilities, applies fixes, and validates results with data quality checks.

This post shows you how to use the Catalog objects API CreateTable() to programmatically create ATHENA and SPARK dialects using cross-account IAM definer roles, and how to add the ATHENA dialect programmatically for the views that were created earlier with only SPARK dialect.

This post focuses on explaining the architecture approach to build the open lakehouse architecture on AWS, unifying the metadata catalog across providers for the AI agents to access. In addition, it highlights the architecture trade-offs and best practices.

Apache Spark Connect bridges the gap between these two worlds: you develop in local Python, but execute on AWS Glue against actual data. Today, AWS Glue interactive sessions support Spark Connect natively. You can connect from any environment that supports the PySpark remote() API, including VS Code, PyCharm, Amazon SageMaker Unified Studio notebooks, and standalone Python applications. You don’t need to install specialized kernels or manage cluster infrastructure.

In this post, we demonstrate how BigBasket implemented the lakehouse architecture on AWS, including their architecture decisions, implementation approach, and the measurable business results you can expect from a similar modernization. Whether you’re facing scalability challenges or planning your own lakehouse implementation, this blueprint provides actionable insights you can adapt for your organization.

In this post, you learn how to build an application log pipeline for production use with Amazon CloudWatch Logs, AWS Lambda, Amazon Data Firehose, AWS Glue, and Apache Iceberg materialized tables. You then use materialized views to accelerate query performance. This solution helps you achieve faster query response times on large-scale log data without requiring you to manage continuous data lake refresh.

In this post, we explore how MDAA transforms data architecture development from months of manual coding to production-ready deployment through configuration-driven infrastructure and embedded governance, examine a real customer transformation, and provide a clear implementation pathway for your own data modernization journey.

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.

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.