Category: AWS Glue

Learn how to pair DuckDB with AWS Glue 6.0 to run SQL-centric ETL on a single worker, reading Parquet from Amazon S3 and writing Apache Iceberg tables to Amazon S3 Tables. This post walks through a complete, runnable example and compares measured cost and runtime against an equivalent Apache Spark job on the same Glue runtime.

Learn how United Airlines uses AWS Glue Data Catalog federation to query Databricks Unity Catalog data directly from Amazon Redshift Serverless without duplicating data, using resource links and AWS Lake Formation for governance.

Accelerate slow, repetitive Apache Spark analytical queries on Apache Iceberg tables without rewriting any SQL. This post shows how automatic query rewrite in Amazon EMR and AWS Glue uses Iceberg materialized views in the AWS Glue Data Catalog to transparently substitute matching query plans, and how to design materialized views for the best speedup.

AWS Glue 6.0 introduces Spark Declarative Pipelines. In this post, you build a single declarative AWS Glue 6.0 job that turns raw order records into validated, aggregated tables through a bronze, silver, and gold sequence, without writing any orchestration logic.

With AWS Glue 6.0, you can build real-time, near-real-time, and batch data pipelines on a single platform. Using a financial market-risk example, learn how to flag high-risk trades with sub-second latency using Spark Real-Time Mode, store heterogeneous pricing vectors with Apache Iceberg v3 Variant columns, and run batch analytics with Arrow-native UDFs.

AWS Glue 6.0 with Apache Spark 4.1 adds support for Apache Iceberg v3: native geospatial types, nanosecond-precision timestamps, the VARIANT type, and DEFAULT column values. This post builds a connected vehicle fleet telemetry pipeline that uses all four in a single Iceberg v3 table, from ingestion through spatial, nanosecond, and variant queries.

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.