Making Big Data Feel Small
DuckDB-powered Cloud Data Warehouse Scaling to Terabytes with Ease
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FINALLY:
A database you don't hate
Why It's Better
Scales Vertically and Horizontally to handle Spikey Workloads
Reads YOUR data. (plaintext, json, parquet, iceberg, xls, csv)
Fast, columnar central data storage optimized for analytics
Run locally, deploy to the cloud for reliability and collaboration. Fits into your workflow
Who is it for?
Analytics that works for everyone
Software Engineers
Who ended up with a big data problem
Data Scientists
Who ended up having to do data engineering
Data Engineers
With slow, brittle pipelines
Who is it for?
Analytics that works for everyone
Software Engineers
Who ended up with a big data problem
Data Scientists
Who ended up having to do data engineering
Data Engineers
With slow, brittle pipelines
Data Warehousing
Is your data all over the place? Start making sense of your business by bringing it together for internal business intelligence and analytics. Build pure SQL pipelines, share data and quickly collaborate with your team.
Customer-facing Analytics
Unlike traditional BI, customer-facing analytics is built directly into your product for end users. It delivers near real-time, low-latency insights at scale — think milliseconds, not minutes — and must handle thousands to millions of concurrent queries. MotherDuck's architecture, from per-user tenancy to Wasm support, is designed for the unique requirements of Customer-Facing Analytics to drive increased user engagement directly in your app experience.
How We Scale
Duckling Sizes
MotherDuck’s per-user tenancy model gives each user an isolated
A Duckling is a dedicated DuckDB instance for each user, ensuring optimal performance and scalability in data analytics.
Pulse
Our smallest instance, perfect for ad-hoc analytics tasks
Standard
Built to handle common data warehouse workloads including loads and transforms
Jumbo
For larger data warehouse workloads with intense transformations or many BI users
Per-user tenancy and vertical scaling
MotherDuck employs a per-user tenancy and vertical scaling strategy. Users connect to their own MotherDuck Ducklings (DuckDB instances), which are sized (pulse, standard, jumbo) to meet their specific needs. There is also the option for additional Ducklings, through read scaling (explained below), to ensure flexible resource allocation. Ultimately, each Duckling establishes a connection with the central Data Warehouse storage.
Read Scaling
MotherDuck's read scaling capabilities allow users to connect via a BI Tool to dedicated Ducklings that function as read replicas. These read replicas can be provisioned in various sizes (pulse, standard, or jumbo) to accommodate different needs. Ultimately, these read replicas connect to the Data Warehouse storage, enabling efficient handling of read operations.
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