What is a Data Mart?

A data mart is a subset of data stored within the overall data warehouse, for the needs of a specific team, section or department within the business enterprise.
For example, a central archive will hold data for the entire business organization, while a data mart makes specific subsets of data available to a set group of users so they don’t have to waste precious time searching the central archive for the data they need.

Data marts make it much easier for individual departments to access key data insights more quickly and helps prevent departments within the business organization from interfering with each other’s data.

Business organizations can take two approaches to establishing data marts:

What is a Data Mart?

More Benefits of Using Data Marts

Data marts also make it possible to generate, collect and analyze data at the source — a process known as edge computing — which helps preserve bandwidth and prevent latency issues.

Mary E. Shacklett, President of research firm Transworld Data, explains:

“By distributing and storing your data into “bite-size” data repositories, you can enhance data aggregation and agility by segmenting manufacturing analytics into specific types of manufacturing and/or geographic regions, without necessarily having to pull data extracts from a centralized corporate database to do the same thing.

You can provide real-time analytics directly to managers at your local manufacturing sites, and you can use the strength of your own internal networks to stream the data.”

Setting up data marts within your overarching data warehouse system removes common business analytics barriers and fuels faster, better decision-making.

Data Marts vs. Data Lakes

You really can’t read about data marts without also learning a bit about data lakes. There is a big conversation about data lakes, and we’ll address that in a separate entry.

For now, here’s what you need to know about data marts vs. data lakes:

Data Marts

  • A data mart offers analytical capability for a restricted area of data
  • Part of a larger data warehouse system
  • Uses “schema on write” system to optimize data for analytical processing

Data Lakes

  • Offers massive storage and agile analytics to a wide variety of users
  • Can be used with or without a data warehouse system
  • Uses “schema on read” approach to answer a business intelligence question
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