In our experience, the primary driver of Snowflake costs is not the compute for aggregation, but the compute required for lots of reads/scans.
We recently built a Snowflake-to-ClickHouse pipeline for a customer where aggregates are built hourly in Snowflake, then pushed into a ClickHouse table to power their user-facing dashboards.
By offloading dashboard queries to ClickHouse, they slashed their Snowflake bill by ~25%, which was worth millions to them.
(Admittedly, running aggregations elsewhere—for example, in Spark—could further reduce costs, but you would then need Iceberg to make the tables queryable in Snowflake.)
I'm in an enterprise environment where a central IT platform team controls what size warehouses we can have in Snowflake. They are not receptive to arguments for larger warehouses, unfortunately. Our issue becomes long-running queries b/c Snowflake spills the data to disk during the joins. TBH, I could join the data more quickly on my laptop than in the warehouse I'm allowed to use. Anyhow, I have then an old build server that is beefy & has 512 GB of RAM, so I can set up my aggregation and/or OLAP services there, since it's an unencumbered playground.
Rill founder here, I have no comment on the UI similarity :) but I would emphasize our vision is building DuckDB-powered metrics layers and exploratory dashboards -- which we presented at DuckCon #6 last month, PDF below [1] -- and less on notebook style UIs like Hex and Jupyter.
Rill is fully open-source under the Apache license. [2]
ydata-profiling does open-source Exploratory Data Analysis (EDA) with Pandas and Spark DataFrames and integrates with various apps: https://github.com/ydataai/ydata-profiling #integrations, #use-cases
Alongside several great talks including Rusty Conover presenting Airport - Arrow + DuckDB — and Christophe Blefari (Bl3f) introducing a new, lightweight orchestrator called yato.
D3.js powers all of the visualizations at Metamarkets, thanks to Vadim Ogievetsky, who got an early look at the D3 code base. It's a powerful framework that extends beyond visualizations: it can be used to attach data to any part of the DOM, not just SVG elements.
We recently built a Snowflake-to-ClickHouse pipeline for a customer where aggregates are built hourly in Snowflake, then pushed into a ClickHouse table to power their user-facing dashboards.
By offloading dashboard queries to ClickHouse, they slashed their Snowflake bill by ~25%, which was worth millions to them.
(Admittedly, running aggregations elsewhere—for example, in Spark—could further reduce costs, but you would then need Iceberg to make the tables queryable in Snowflake.)