AWS Big Data Blog

Today we are announcing that Amazon has signed a definitive agreement to acquire DuckLabs, the Amsterdam-based company behind the open-source analytical database DuckDB. We expect the transaction to close shortly, subject to customary closing conditions. Hannes Mühleisen and Mark Raasveldt, who created DuckDB and co-founded DuckLabs, will continue leading the team and the open-source project’s technical direction as part of AWS. The DuckDB open-source project will also continue to be driven by the DuckLabs team, remain open source under the independent Foundation (the non-profit that oversees DuckDB), and available under the MIT license as it does today.

You can now use PythonOperator and BashOperator to run custom Python functions and shell scripts directly in the Amazon MWAA Serverless runtime, without provisioning additional infrastructure. This post walks through building a serverless pipeline that converts CSV files to JSON using a PythonOperator and verifies the output with a BashOperator.

Your Google BigQuery users need to query data that lives in Amazon S3 Tables on AWS without copying it across clouds. This post shows how to connect BigQuery to Amazon S3 Tables through the AWS Glue Iceberg REST Catalog using IAM-based access control, so you keep one governed dataset and query it live from BigQuery.

In Part 2 of this series, connect Google BigQuery to Amazon S3 Tables using AWS Lake Formation credential vending. Lake Formation manages fine-grained permissions and issues short-lived, scoped credentials to external engines, so you can centrally govern which teams and query engines read your Iceberg tables on AWS without managing IAM policies for every consumer.

Amazon EMR on EKS now runs Apache Spark up to 3.7x faster on Amazon EC2 G7 instances with NVIDIA RTX PRO 4500 Blackwell GPUs than on comparable CPU instances, with no changes to existing Spark code. See the TPC-DS benchmark results, the cost comparison, and how to get started.

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.

Amazon Redshift system table integration with Amazon S3 Tables automatically delivers your system table logs to Amazon S3 Tables in Apache Iceberg format. You can retain this data well beyond the 7-day limit for compliance, auditing, and cross-warehouse observability, without custom ETL pipelines or cluster resource consumption.

Learn how to track Amazon SageMaker Unified Studio project costs by custom tags. This serverless solution enriches AWS Cost and Usage Report (CUR) data with custom project tags and visualizes cost by CostCenter, Team, or Environment in an Amazon Quick Sight dashboard.

Learn how to secure Amazon SageMaker Unified Studio by integrating it with an external SAML identity provider such as Okta. This post shows you how to apply conditional access policies that enforce device compliance, IP-based restrictions, and multi-factor authentication for your data and AI workloads.