In past jobs, I (Rick) rolled out Snowflake at a manufacturer with 11000 employees and $2B in revenue, and Théo built the data team and infrastructure at a B2B company that raised $200M. My experience introducing Snowflake was that after 2 years and more than 1000 tables and many more columns, it was hard to find data and tedious to understand it, and users would not trust the data without a lot of upfront due diligence.
We index all data and visualization assets that are connected, making it easy to find them. We profile data continuously to show what's inside, how it is changing, and make it easy to document and tag information. What helps us surface a lot of context is that we parse Snowflake's query history. This lets us provide automated data lineage and indicate data freshness and usage.
Most of our customers that see their actual data flows are surprised by the complexity they have built. Some see for the first time how their data flows work and how many redundant data assets they have created from one base table.
For now, we focus on the Snowflake ecosystem and don't support the long-tail of other database and analytics solutions. This lets us "overfit" to the needs and problems of companies that sit on this stack, e.g.: Fivetran > DBT > Snowflake > Tableau. Under the hood we leverage the flexibility of ArangoDB to offer a fast application that offers great search speed and also graph workloads e.g. for data lineage.
Our initial market is 5k Snowflake customers, but the global data warehousing market consists of 250k companies. We charge per indexed data asset. The number of data assets in companies is doubling on average every 2 years, so this market is taking off.
We offer both cloud and self-hosted. Self-hosted means a Docker Compose configuration that starts 3 Docker containers. The only data that is transmitted to our system is a license check in this case.
Your questions and comments are welcome. Thank you!