Quicker, cheaper, safer. Pick Three
Have more of your company relying less on your data warehouse, with sophisticated modelling and caching.
“A deal breaker in nowadays life where we are bombarded with thousands and thousands of tools that PUSH you on how to use more and more compute, to spend credits and generate large dollar bills this tool shines out with a different approach.”
Everything you need: you, your data, and DuckDB.
Once you've queried data into the canvas, queries downstream run completely in the browser without touching your database again. Exploration is lightning quick and supercharged by DuckDB's expressive analytical dialect. Count users find ~80% of their queries running in the browser and off the datawarehouse.
A semantic layer to empower self-service
Use our powerful semantic layer Count Metrics to either define your core analytic definitions, or wrap your existing external models in robust user-land caching. Share data with confidence knowing that aggregations and units are predefined, and governed with full version control.
Save time and money with caching control
Cache data at the database, metric catalog, canvas, or query-level to make sure your data-warehouse is being used effectively and economically, while never worrying about stale data in key canvases.
Validate each catalog commit before you make it
Create and test catalog branches before deploying
Symmetric Aggregations keep things straight as you group and filter
Set permissions on catalogs to segment data between user groups.
Cache views to speed up every canvas
Keep everyone working from the same data with scheduled caching
Related Resources
Frequently Asked Questions
Our novel use of DuckDB in the browser and the server lets customers offload ~80% of day-to-day queries off your database, drastically reducing compute costs. In addition, Count has flexible and fine-grain caching controls so you can keep data current, while ensuring that expensive queries are run only when needed. Once your catalogs are setup in Count Metrics, you can cache entire views within DuckDB to enable low-cost self-serve at scale.
Count Metrics brings first-class semantic analysis capabilities directly into Count, automatically detecting references, flagging errors, and alerting users to inconsistencies across reports.
While other semantic layers often sacrifice flexibility for governance, we refused that trade-off. Metrics and dimensions defined within Count Metrics can be used seamlessly within Count’s visualization and low-code features, and then later referenced by any cell, including SQL and Python cells.
Our use of DuckDB on the server allows us to go further than accelerating individual or common queries, instead we can cache the entire data model, allowing for all downstream queries to run outside of your data warehouse.