Thinking Small: How Motherduck Has Positioned Itself at the Epicenter of the Small Data Movement
The data world has spent the last decade chasing scale. Bigger pipelines, bigger clouds, bigger bills. But at Small Data SF 2025, a new conversation takes center stage: maybe “thinking small” is the smarter move.
Many of the data products available today can crunch through serious amounts of data. Systems that are cloud-based can scale “forever” and dynamically adapt to workflows both large and small.
However, it seems like we’ve all been future-proofing for a big data future that never arrived. If you already know that your data is small, you don’t need all of the advanced features (and costs) that come with big cloud data platforms. There are some products like Snowflake that only charge based on directly measured usage time, but you’re still learning to drive a tractor-trailer just to take a stroll around the block.
Why Teams are Thinking Small
At this year’s Small Data SF, hundreds of engineers and analysts wondered together:
“Am I working with small data? How can I use the right tools to save money and reduce complexity? Is there any merit in picking small data tools vs something like Snowflake that can expand to any data size? ”
MotherDuck is helping to answer this question. Both as the host of Small Data SF and as a product, MotherDuck is reframing what “scalable analytics” really means- by starting small and working up from there.
The Case for Small Data Tools
The straightforward benefit of working with small datasets is predictability.
File sizes are smaller.
Row counts are lower.
You can almost always use the smallest hardware configuration available (think x-small Snowflake warehouses).
But, the less obvious benefit is that you don’t have to outsource each of the data operations to a remote system. In the past, this would be done because the local system couldn’t handle the massive workloads that large organizations need for their daily operations and reporting needs.
DuckDB makes it possible to bring some of those workloads back local again. If you truly have small data, you can store raw data, transform it with an engine like dbt, and write back the output into a local DuckDB file, all without leaving your laptop.
Three signals of a true small data project:
- Your full data history fits on a laptop
- You have enough RAM to run the transformation models
- You are the primary consumer of your own analytics
Collaboration Challenges (and How MotherDuck Solves Them)
The biggest challenge I see with small data is learning how to work effectively with a team. In the “single player” approach where you run everything in DuckDB by yourself, it’s not clear how you would share that data with others or load it into a BI product. Your DuckDB file might have all the analytics they want to see, but you don’t want to have them remotely accessing your laptop whenever they want to view it.
Motherduck aims to fill this gap by acting as the cloud-hosted checkpoint for all of your DuckDB files. It lets you:
Attach DuckDB files to a shared, cloud-based workspace
Enable others in your org to query and visualize data remotely
Balance local compute with collaborative access
I wouldn’t quite call it a fully-featured data warehouse, but it’s a great system for distributing workflows that are built with DuckDB (or even DuckLake). With this platform, it’s possible to attach DuckDB files to the cloud MD service, allowing others in your org to attach to that same database from within their MD account, or from the DuckDB CLI.
You can also run your small data workflows on Motherduck entirely, at rates that compete with Snowflake and other similar cloud tools. (Fun fact: Warehouses are called Ducklings in MD). Through this architecture, Motherduck supports a blend of cloud and local workflows, and makes it easy to switch between them.
The Future: Thinking Small to Move Fast
The thinking we’ve used to create our current cloud data pipelines is not going to bring us into the next era of data architecture. Cloud warehouses push us to keep thinking bigger (auto scale out + scale up, serverless compute, infinitely expandable storage, etc).
Motherduck and DuckDB open up the opportunity to think small again and revisit some of our old workflows to make them run more efficiently and utilize more local equipment.
Next Read: A Deep Dive on MotherDuck
If this post sparked your interest, check out our full review of Motherduck: