Data Mart vs Data Warehouse

What's the diff?

Q: What is the difference between a data mart and data warehouse? When should you use a data mart and when should you use a data warehouse?

A: Generally speaking, a data mart consists of a small subset of data in a data warehouse. The distinction and design difference became an industry standard before the era of horizontal scaleout architecture. In those days, data warehouses were generally run on ‘Enterprise’ class hardware, meaning mainframes, and data marts were run on ‘departmental’ class hardware, meaning servers.

Most of the current implementations in DW is run from that legacy architecture, but the newer generation of DWs allow for horizontal scaling and clusters with failover capabilities. Improvements in data management and database design essentially means that data warehouses and datamarts can be unified. Today with databases like Redshift, Vertica and Snowflake there is no reason to architect two different models on different hardware platforms to serve the enterprise and departments alike.

The more advanced data architectures enable realtime reporting across platforms like Kafka and other message bus based technology. Integration of legacy Hadoop is also a done deal. Similarly advanced analytics and machine learning tech is integrated, and shortly LLMs will join these next generation data management stacks.

You should use a datamart when you cannot get political access to your DW. You should always use a DW. This choice is no longer about technological capability.