Announcements, Data Reliability

Delivering Reliable Data and AI Pipelines with Monte Carlo and MotherDuck

Monte Carlo Motherduck DuckDB

Tim Osborn

Tim is a content creator at Monte Carlo who writes about data quality, technology, and snacks—occasionally in that order.

The DuckDB hype is real — this in-process analytical database has skyrocketed in popularity over the last few years. Known for its columnar storage, vectorized query execution, and scale-up approach to SQL analytics, DuckDB fans proclaim it’s faster, more efficient, and more affordable than other databases. 

DuckDB is also becoming a must-have layer in many AI stacks. As more organizations race to adopt GenAI and build AI-powered data products, DuckDB provides emerging applications as the storage layer of RAG knowledge bases to streamline and expedite data management. 

MotherDuck turbocharged DuckDB’s efficiency with multiplayer cloud analytics, making it a lightweight but powerful data warehouse. 

Since we’re on a mission here at Monte Carlo to help companies achieve data and AI reliability, it made perfect sense to add MotherDuck to our flock of partners. We’re proud to announce the official integration between MotherDuck and Monte Carlo.  

Scaling data reliability across the modern data and AI stack

Consistently recognized as G2’s #1 Data Observability Platform, Monte Carlo helps hundreds of organizations improve and protect data quality with end-to-end visibility across the entire data and AI stack. Our platforms provide automated monitoring and alerting, as well as field-level lineage, to help data teams be the first to know when data pipelines break and speed up incident resolution. 

And it’s absolutely vital for MotherDuck users to know when data quality issues occur. Designed to be a user-friendly data warehouse, MotherDuck is often leveraged by data teams and software and app developers, for business  analytics and to build interactive data apps. This means that when data quality issues occur, important workflows and data products can get derailed. 

With Monte Carlo monitors in place, the relevant stakeholder teams will be alerted immediately — allowing them to minimize the negative impact of data downtime and maintain data trust. 

How the Monte Carlo and DuckDB integration works

Now, Monte Carlo can automatically monitor, alert, and triage data quality issues across DuckDB — for both data product and AI workloads. This includes MotherDuck, the serverless data warehouse built on DuckDB that raised a $52.5M Series B round in September 2023.

The integration uses custom SQL monitors to expand data quality coverage across MotherDuck databases. Data teams can set up these monitors in the easy Monte Carlo UI wizard, programmatically via monitors as code, or both. 

Once in place, Monte Carlo’s monitors can be used to generate alerts when data pipelines break, intelligently routed to relevant stakeholders. By extending data observability across MotherDuck databases, teams can drastically reduce the time to detection and time to resolution for data quality issues.

Learn more about DuckDB and Monte Carlo

If you’re already using MotherDuck and Monte Carlo, it’s time to start safeguarding your data quality. Check out our documentation to learn more. 

If you’re new to data observability, welcome! We can’t wait to show you how our platform can ensure your data reliability at every stage of the pipeline. Get in touch for a personalized demo today.

Our promise: we will show you the product.