Build consistent metrics definition
Define your company’s business logic in Holistics modeling layer to be used by the entire company.
Define your data model once and reference any piece of it anywhere else to avoid writing the same SQL queries over and over again.
As your business changes, updated metrics keep everyone aligned around fresh data.
Perform self-service analysis based on modeled data
Business users now can perform self-service analysis without knowing SQL or relying on data teams.
What makes Holistics data modeling different?
SQL as underlying modeling engine
Holistics data modeling layer is built completely on top of SQL, translating every action into a SQL query that eventually runs against the MPP databases to query live results.
This utilizes the power of MPP data warehouses and prevents data duplication in traditional approach.
Unify ELT + BI
With the rise of MPP, more people are loading raw data into data-warehouses before transforming (ELT), then use SQL to transform and run analysis.
Holistics implement this idea by using SQL to unifying the Transform and BI step together, covering the entire analytics pipeline from end to end.
Best practices from software development
Holistics copies ideas expressed from DevOps in software development, and apply them to analytics. This speed up BI deployment, increase reliability and quality of analytics.