Data warehouse vs Data Mart Vs Data Lake

• Data warehouse
  • Aggregation of data collected from multiple sources to a single central repository that unifies the data quality and format.
  • Highly curated data that serves as the central version of the truth
  • Meant to store structured data.
  • Mostly used for BI, Analytics, Data mining, Artificial Intelligence(AI) and machine learning
  • Example of use cases – Big data integration, NLP, Auditing, Reporting Systems, Tactical business analytics etc.
  • Size: Typically larger than 100 GB 

• Data Mart
  • Subset of a data warehouse that benefits a specific set of users within the business or business unit.
  • Used to segment a large data warehouse into operable ones
  • Subset of data held in a data mart typically aligns with a particular business unit like sales, finance, or marketing
  • Example of use cases – A financial analyst can use a finance data mart to carry out financial reporting
  • Size: Typically less than 100 GB

• Data Lake
  • Large repository of raw data , contains structured , semi-structured or unstructured data.
  • Data is aggregated from various sources and is simply stored
  • Not built to suit a specific purpose or fit into a particular format
  • Stored any data that may or may not be curated (ie. raw data)
  • Example of use cases- Machine Learning, Predictive analytics, data discovery and profiling
  • Size : Typically in PBs

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