Case Study: Clear Street uses Metaplane to prevent $100M+ in data quality issues
Key results
The challenge
Clear Street runs a cloud-based modern data stack on Snowflake and Sigma that powers decision-making for a prime brokerage platform moving large volumes of money daily. With hundreds of attributes per financial instrument, a single incorrect value can change downstream valuations dramatically and, multiplied across users and transactions, produce significant losses. Although there were no pressing incidents, the team knew the risk of data quality issues would grow as the stack scaled.
The solution
Clear Street evaluated open-source and paid options before choosing Metaplane for its data quality monitors, integrations, and column-level lineage. The team integrated Snowflake, Sigma, dbt, and Fivetran, and deployed over 300 monitors tracking freshness, row count, cardinality, numeric distributions, and string formatting on critical objects such as ticker symbols.
“Metaplane is the data quality x-ray on our data stack.”
DWDavid WassermanSenior Data Architect, Clear Street
The results, in context
Metaplane is positioned as the data quality layer that helps Clear Street prevent $100M+ worth of data quality issues on its platform. The team deployed more than 300 data quality monitors with 100% coverage of its dbt jobs, and used row-count monitoring to catch a client generating an anomalously high number of records, prompting a proactive customer conversation. Column-level lineage also supported a legacy-database deprecation by mapping downstream dependencies.