Amazon has acquired DuckLabs, the Amsterdam company behind DuckDB, and AWS has embedded DuckDB directly into Amazon Aurora PostgreSQL. The integration lets supported Aurora databases query live PostgreSQL records—including uncommitted writes—together with Iceberg and Parquet data in Amazon S3 through one SQL interface, without a separate ETL path for that analysis.
What Amazon acquired and when it took effect
DuckLabs announced on 26 August 2026 that it would join Amazon Web Services. The transaction process concluded on 31 August and became effective on 1 September 2026. AWS described the deal as the acquisition of the Amsterdam company behind the open-source analytical database DuckDB.
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DuckLabs says its team remains based in Amsterdam and continues developing DuckDB, DuckLake, Quack and the surrounding ecosystem. Founders Hannes Mühleisen and Mark Raasveldt continue to lead the team and the technical direction of the open-source project inside AWS. DuckLabs reports more than 30 employees and more than three million DuckDB downloads per day.
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In announcing the deal, AWS Big Data Blog vice president Mai-Lan Tomsen Bukovec said Amazon had signed a definitive agreement to acquire “the Amsterdam-based company behind the open-source analytical database DuckDB.”
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DuckDB remains open source after the acquisition
The acquisition does not change the licensing or governance stated by the project. DuckDB, DuckLake, Quack and related components remain free and open source under their existing MIT licenses. The independent nonprofit DuckDB Foundation continues stewardship of the project.
The DuckDB project announcement from Mark Raasveldt and Hannes Mühleisen says the projects will remain open source under the MIT license, with no announced changes to the roadmap, licensing or governance. DuckLabs founder updates likewise say the foundations of the project remain in place. AWS ownership therefore changes the corporate home of the development team, not the published license or the Foundation’s role.
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What AWS added to Aurora PostgreSQL
On 30 September 2026, AWS announced that DuckDB is embedded directly within Aurora PostgreSQL. A supported Aurora instance can run a query that combines operational PostgreSQL data with data-lake files and tables, rather than exporting one side into a separate analytical system first.
The integration can read Apache Iceberg tables and Parquet data stored in Amazon S3, S3 Tables or cataloged through the AWS Glue Data Catalog. AWS says processing remains within Aurora, reducing extra network hops and avoiding pipelines that copy or duplicate data before analysis.
Because the operational side is queried in place, the statement can include live changes, including uncommitted writes. That matters for use cases such as a dashboard that joins the latest order or inventory transaction with historical lake data, or an application and an AI agent that need current state and archived context through a familiar PostgreSQL interface.
Pre-acquisition architecture versus the embedded capability
| Architecture question | Separate operational and analytical systems | Aurora PostgreSQL with embedded DuckDB |
|---|---|---|
| ETL or reverse ETL | Combining PostgreSQL records with lake data generally required moving, replicating or exposing data through ETL or reverse-ETL workflows. | For the documented query path, Aurora can analyze both locations directly, removing the extra copy-and-load step. |
| One SQL statement | Operational and lake queries were commonly issued in different systems and then reconciled. | One Aurora SQL statement can combine live PostgreSQL rows with Iceberg or Parquet data. |
| Operational data freshness | A copied analytical table could lag the source database and would not naturally include uncommitted transactions. | Aurora can include current operational data, including uncommitted writes, in the same query. |
| Lake sources | Coverage depended on the connectors and engines deployed separately. | AWS documents Apache Iceberg, Parquet, Amazon S3, S3 Tables and AWS Glue Data Catalog sources. |
| Network and duplication | Separate engines and transfers added network hops and could create duplicate datasets. | AWS says processing stays within Aurora, avoiding those additional hops and duplication for this capability. |
| Governance and licensing | DuckDB was already MIT-licensed and Foundation-stewarded. | The acquisition leaves the stated MIT licensing and Foundation stewardship unchanged. |
This does not make every data-integration job disappear. Organizations may still need ingestion, transformation, quality controls or governed copies for workloads that are not covered by Aurora’s embedded query path. The change is that a supported Aurora query no longer needs those steps merely to join current PostgreSQL data with the listed lake formats and catalogs.
Which Aurora PostgreSQL versions support it
AWS lists support for the embedded DuckDB capability on:
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- Aurora PostgreSQL 18.6 and later in the 18 series.
The version numbers identify the minimum releases stated in AWS’s announcement; an older Aurora PostgreSQL minor version should not be assumed to include the feature.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to enable the feature
There are two documented prerequisites: an IAM role configured with the AuroraAnalytics feature and the aurora_analytics extension in the database.
- Run an Aurora PostgreSQL cluster on version 17.11 or a later 17.x release, or version 18.6 or a later 18.x release.
- Create or select an IAM role and configure it with the
AuroraAnalyticsfeature, granting the permissions required for the S3, S3 Tables or Glue Data Catalog data that the cluster must read. - Install or enable the
aurora_analyticsextension in the target Aurora PostgreSQL database. - Use the Aurora PostgreSQL connection to issue the documented DuckDB-enabled query pattern against operational tables and the required Iceberg or Parquet sources.
- Validate access with the least-privilege IAM policy and test the query against the exact catalog, bucket, table and file locations used by the application.
The announcement establishes the required role feature and extension, but it does not publish a universal IAM policy or a single SQL template that applies to every catalog and storage layout. Those details depend on the AWS resources and permissions in a particular account.
What this means for real-time analytics
Dashboards that need current transactions
A dashboard can combine a live operational fact—such as a transaction that has not yet been committed—with historical records in the lake. That can reduce the delay and reconciliation work associated with periodically refreshed reporting tables.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsTransaction enrichment
Applications can enrich a current PostgreSQL result with older events, reference data or large Parquet datasets without first loading those files into ordinary relational tables. The value is greatest when the question requires both immediate state and a broad historical window.
AI agents using PostgreSQL access
AWS positions the feature for AI agents that need to reason over live and archived data through a PostgreSQL interface. The agent still needs appropriate database permissions, query controls and output validation; embedding DuckDB supplies the data-access path, not an automatic security or governance policy.
Quick Recap
What developers and open-source users should watch
- Licensing: The stated MIT licenses remain in effect for DuckDB, DuckLake, Quack and related components.
- Project leadership: Mühleisen and Raasveldt continue directing technical development, with the team remaining in Amsterdam.
- Deployment scope: The embedded feature is an Aurora PostgreSQL capability tied to the specified engine versions; it does not mean every PostgreSQL installation automatically contains DuckDB.
- Data access: Reading S3, S3 Tables or Glue-cataloged data still depends on correctly configured IAM permissions and resource policies.
- Workload fit: Joining live rows to lake data can reduce movement, but teams should still evaluate query performance, concurrency, cost and governance for production workloads.
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