Amazon Acquires DuckDB Creator DuckLabs, Adding Analytical Engine to Aurora
Amazon has acquired DuckLabs, the team behind DuckDB, and introduced native analytical query capabilities inside Amazon Aurora PostgreSQL.
Amazon has completed the acquisition of DuckLabs, the Amsterdam-based startup responsible for the analytical database engine DuckDB. As part of the transition, DuckDB co-founders and creators Hannes Mühleisen and Mark Raasveldt will join Amazon Web Services (AWS) to continue guiding the team and steering the technical direction of the open-source initiative.
Writing on their blog, Mühleisen and Raasveldt stated that the partnership provides the necessary backing to expand their project's footprint: "Joining AWS gives us the resources and reach to bring this technology to many more developers and organizations, and to pursue ideas at a scale that would have been difficult for us to reach alone."
Open-source governance and background collaboration
Addressing potential community concerns, AWS and DuckLabs confirmed that the software ecosystem will maintain its independent governance. DuckDB, alongside companion tools in the "Duck Stack" such as DuckLake and Quack, will remain free and open source under the permissive MIT license, supervised by the non-profit DuckDB Foundation.
Reaction across the developer ecosystem has been largely supportive, alongside typical caution regarding corporate acquisitions. Community member adappergentlefolk shared on Reddit: "I hope this doesn't slow the pace of innovation they have been doing, but i'm glad they found a way to both cash in on their work yet keep most of it oss under the foundation."
Andy Warfield, distinguished engineer at AWS, noted in an article on Werner Vogels's blog that the two teams have collaborated closely for roughly two years. The connection started when AWS was developing S3 Tables. Over time, technical discussions expanded into exploring AWS Lambda as a compute foundation for launching DuckDB queries and refining database connections via DuckDB's ATTACH and CONNECT commands. Warfield explained that the alignment between both groups led to the acquisition, seeking to provide Mühleisen and Raasveldt a larger platform to shape cloud-native analytics architecture.
Targeting sub-terabyte queries and agentic AI
AWS leadership emphasized that DuckDB addresses a distinct, ubiquitous class of analytical workloads. Mai-Lan Tomsen Bukovec, technology vice president at AWS, highlighted that the engine "set out to solve the problem of blazingly fast performance for the 90%+ of data queries in the world today, that often runs 1 terabyte of data or less as part of analysis and dashboarding."
Bukovec also pointed to the rise of autonomous software systems, explaining that DuckDB's architecture matches agentic AI workflows. Because AI agents regularly inspect tabular data and execute rapid exploratory queries before deciding their subsequent actions, low-latency, embedded execution becomes vital. As teams build these intelligent workflows, developers can try top AI models cheaply through one API at https://apixoai.online to power agent logic alongside fast data retrieval.
Aurora PostgreSQL gets native analytical execution
The acquisition has produced immediate product integrations, with Amazon Aurora PostgreSQL becoming the first AWS service to launch a user-facing capability powered directly by DuckDB.
Esra Kayabali, principal solutions architect at AWS, noted that querying mixed environments historically carried heavy operational overhead: "Previously, if your application needed to combine recent transactional data in Aurora with historical records stored in Amazon S3, a common approach was to build reverse ETL pipelines that duplicated data, increased infrastructure costs, and required ongoing engineering effort to keep everything synchronized."
With DuckDB built into Aurora PostgreSQL, developers can run single queries that span live transactional tables and historical datasets stored in Parquet or Apache Iceberg formats within Amazon S3 and S3 Tables, including metadata managed by AWS Glue Data Catalog. Because execution occurs locally within the Aurora instance, systems avoid auxiliary network hops and external pipelines. The capability is currently live in all commercial AWS regions and AWS GovCloud (US), incurring no separate licensing costs beyond normal Aurora compute utilization and S3 request fees.
What it means for developers
For engineering teams building on AWS, this development addresses persistent friction between transactional databases and cloud data lakes:
- Elimination of complex ETL: Developers can query live relational rows in Aurora and historical archives in S3 concurrently within a unified SQL statement, removing the necessity of maintaining duplicate sync pipelines.
- Lower operational latency: Analytical operations remain contained within the database engine, avoiding the network overhead and external compute costs typically associated with federated queries.
- Preserved open-source autonomy: Because DuckDB remains MIT-licensed under the DuckDB Foundation, developers using the engine in local or independent environments can continue doing so without vendor lock-in concerns.
- Optimized agentic workflows: Systems running AI agents can carry out quick, sub-terabyte exploratory queries directly against lakehouse and transactional storage without spinning up heavy data warehousing infrastructure.
Source: Amazon Acquires DuckLabs and Integrates DuckDB in Amazon Aurora PostgreSQL — InfoQ AI & ML. Written by the Apixo team from that report.
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