5 Reasons Why We’re Excited About MotherDuck Launch in AWS Frankfurt

5 Reasons We’re Excited About MotherDuck’s Launch in AWS Frankfurt

For some time, a key challenge for European data teams has been balancing innovation with strict regulation. We’ve often seen powerful tools launch first in the US, while our need for GDPR compliance and data sovereignty requires a local presence.

This is why the official launch of MotherDuck in the AWS Frankfurt region on 24 September (eu-central-1) is a noteworthy development for the data community here ¹. As an official launch partner, we evaluated the platform ahead of its European debut. Our perspective is clear: this is far more than just a new cloud location. It represents a new, more efficient philosophy for small- to medium-sized data analytics.

Before we dig into the five key reasons for our positive assessment, let’s first address the fundamental question:

What the Duck Is MotherDuck?

To fully appreciate MotherDuck's value proposition, one must first understand its foundation: DuckDB.

In essence, DuckDB is to analytics what SQLite is to transactional databases – a powerful, serverless, and embedded data engine. It operates “in-process,” which eliminates the need for any complex client–server architecture. There is no server to install, no instance to configure. This simplicity, combined with its consistently high rankings in performance benchmarks like ClickBench², has led to widespread adoption (over 30k GitHub stars) and a massive developer following.

While DuckDB excels on a local machine, enterprise environments require additional capabilities for collaboration, security, and scale. This is precisely where MotherDuck comes in. It extends DuckDB with a suite of production-ready, cloud-based features, including:

• Centralized authorization and user management
• Durable, persistent cloud storage
• Intelligent caching and query scaling

As MotherDuck’s founder, Jordan Tigani, often puts it: “While DuckDB turns your laptop into a personal analytics engine, MotherDuck scales your laptop into the cloud with dual execution.” In short, MotherDuck provides the collaborative and enterprise-grade layer that allows teams to harness the full power of DuckDB in a secure and scalable way. More on dual execution below.

The architectural approach of combining a powerful local engine with a lean, collaborative cloud layer is what makes the platform particularly compelling. Based on our evaluation, here are five key reasons why we are excited about this development:

Your Data Most Likely Isn’t as Big as You Think

The data industry has been obsessed with “Big Data” for over a decade. But as MotherDuck’s founders argue, Big Data Is Dead. The reality is often simpler: companies don’t operate at the petabyte scale of Google or Meta. Many analytics workloads run on datasets that are gigabytes or a few terabytes in size. This scale doesn’t always require massive, complex distributed systems.

Today’s laptops and single servers are incredibly powerful—or, as DuckDB creator Hannes Mühleisen puts it, we lived through a “lost decade of small data.” MotherDuck and DuckDB are engineered for this small- to medium-sized data reality. They provide the power of a distributed system without the overhead, cost, and complexity, allowing you to process data efficiently where it makes the most sense.

A Focus on Simplicity and Performance

One of the biggest barriers in data analytics is often the initial setup. Traditional systems require installing servers, configuring instances, and managing permissions before a single query can be run. DuckDB’s core mission is to eliminate this friction entirely.

There are no servers to manage. You simply install the library and start querying. This radical simplicity, combined with its high-performance query engine, changes the time-to-insight. An analyst can go from a 10 GB Parquet file to meaningful results in seconds, not hours (depending on hardware and data layout). MotherDuck extends this philosophy to team collaboration, providing the necessary infrastructure for production-ready data applications without reintroducing the complexity that DuckDB was designed to remove.

Simplified and Secure Data Sharing

A common challenge in collaborative analytics is the manual exchange of data files, such as CSV exports. A practice that still kicks off many data science and AI projects. This approach creates data silos, raises security concerns, and leads to version-control issues with inconsistent datasets spread among different team members.

MotherDuck directly addresses this by changing the paradigm from sharing data to sharing access to data. Instead of creating data copies, teams can work from a single, consistent source of truth.

The sharing model, accessible via a web interface and controllable programmatically with standard SQL commands (e.g., GRANT/REVOKE semantics), eliminates the need for “nasty CSV exports” and provides a clean way to collaborate.

Rich Partner Ecosystem

A data platform’s value is directly tied to its ability to integrate with the tools an organization already uses. MotherDuck embraces a “bring your own tools” philosophy, standing in contrast to more monolithic platforms.

The platform is supported by a rich and growing ecosystem of partners, including numerous open-source projects and commercial SaaS solutions. This commitment to interoperability allows organizations to pick and choose a best-of-breed data stack tailored to their specific needs, rather than being forced into a one-size-fits-all solution.

Dual Execution: Local Speed and Cloud Scale

Perhaps the most technically innovative feature of the platform is its approach to dual execution. MotherDuck’s intelligent query planner can distribute a single query plan between a local DuckDB instance and the MotherDuck cloud service.

This model leverages the best of both environments: the interactive speed and low latency of local processing with the scalability and durable storage of the cloud. By executing parts of a query on the user’s machine, this architecture minimizes data transfer by design, which in turn reduces both latency and cloud compute costs.

This powerful dual execution model even extends to modern web applications, where DuckDB can run directly in the browser (via WebAssembly) while being powered by MotherDuck’s backend for larger, shared datasets.

Conclusion

SQLite revolutionized the world of transactional databases. By providing a powerful, serverless, file-based database, it became an integral part of nearly every application and device on the planet – for examples, see here.

We believe DuckDB is poised to do for analytics what SQLite did for transactions. It brings the same principles of simplicity and high performance to a domain that has long been burdened by complexity. MotherDuck is the catalyst that scales this potential, transforming DuckDB from a personal powerhouse into a viable, collaborative platform for modern data teams.

The official launch in Frankfurt is an exciting milestone for data analytics in Europe. We are proud to be a launch partner on this journey and look forward to seeing the innovative solutions our clients will build with this technology.

What’s Next? If you are exploring ways to modernize your data stack or want to see how this new approach could benefit your team, we are here to help. Contact us for a no-obligation consultation to discuss your specific use case.

1. As with any cloud service, customers should verify whether any control-plane metadata or telemetry leaves the region if this is a requirement. ↩

2. Please take performance benchmarks with the usual grain of salt, as the complexities are thoroughly explained here by DuckDB co-creator Hannes Mühleisen: On Performance-Testing in Database Systems. ↩