AWS Big Data Blog
AWS and DuckLabs: Building the future of analytics together
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.
Razor Group’s journey to a modern data lakehouse on AWS
Razor Group, one of Europe’s leading ecommerce aggregators managing 250+ brands, migrated from always-on Amazon Redshift clusters to an open lakehouse on Apache Iceberg, Amazon S3 Tables, and Apache Spark. Learn the architectural decisions, the five-phase migration, and the results: 65% faster P95 queries and a 63% infrastructure cost reduction.
How Picnic configured multiple OAuth providers for Amazon MQ
Picnic runs RabbitMQ as the messaging backbone for hundreds of microservices on Amazon MQ for RabbitMQ. This post shows how to configure one broker to trust multiple OAuth 2.0 identity providers, Keycloak for operators and AWS IAM for services, so you can eliminate static credentials while maintaining separate identity paths for people and workloads.
Build with geospatial and variant types in Iceberg v3 on AWS Glue 6.0
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.
Migrate an OAuth 2.0 authenticated Apache Kafka cluster to Amazon MSK with MSK Replicator
MSK Replicator now supports OAuth 2.0 (SASL/OAUTHBEARER) authentication to external Apache Kafka clusters. This post walks through the three supported grant types, how to configure Replicator for each, the network and TLS prerequisites that are commonly missed, and how to handle identity providers behind an additional federation layer.
Announcing in-place ZooKeeper-to-KRaft cluster upgrades for Amazon MSK
Amazon MSK now supports in-place upgrades from ZooKeeper to KRaft metadata mode. You can modernize your existing cluster’s metadata management through the familiar version upgrade workflow, with no new cluster to provision and no data migration. This post covers the prerequisites and the step-by-step upgrade process.
Amazon MSK Service 101: How many partitions does an Amazon MSK topic need?
How many partitions does your Amazon MSK topic need? Choosing the right partition count affects throughput, scalability, and operational complexity. This post provides practical guidance for sizing partitions, covering per-partition throughput, consumer parallelism, partition keys, and Amazon MSK partition-per-broker guidelines.
PythonOperator and BashOperator Now Available on Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Serverless
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.
Enable cross-cloud analytics with Amazon S3 Tables and Google BigQuery, Part 1: IAM-based access control
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.
Enable cross-cloud analytics with Amazon S3 Tables and Google BigQuery, Part 2: access control with Lake Formation
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.









