Data Mart

Data marts are functional subsets of data that are stored in a data warehouse. It can be regarded as a condensation version of a data warehouse built to serve a specific department or unit. It generally draws data from fewer sources than a data warehouse, is smaller and more flexible.

Data marts have an objective to improve the user’s response time due to reducing the amount of data and complexity. Within data marts, data is partitioned according to the user’s needs and offers a more granular level of access control. It also serves as a means for users to leverage self-service data tools connected to the data mart.

The Three Types of Data Marts

  • Dependent data marts are created by extracting data directly from several data sources. A dependent data mart commonly sources data from a single master data warehouse. Therefore it offers the benefit of centralization. Generally, if it’s needed to develop one or more physical data marts, the best option is to configure them as dependent data marts. Data engineers can build dependent data marts in two different ways. Either users can access both the data mart and data warehouse, or they can be limited to just the data mart. It is necessary to be careful with the second approach as it might quickly decay into a data junkyard.
  • Independent data marts are created without the need for a central data warehouse. This kind of data mart is an ideal option to serve departments or small units within an organization. These teams have neither a relationship with the enterprise data warehouse nor with any other data mart. In an Independent data mart, the data is input separately, and its analyses are also performed autonomously.
  • Hybrid data marts draw data both from data warehouses and other data systems. It seeks to make use of other sources apart from the data warehouse. Hybrid data marts are helpful institutions that require ad-hoc integration, like when a new unit or department has been added to an organization. It is well suited for multiple database environments and a fast implementation turnaround for any organization as it requires low data cleansing complexity. Hybrid data mart also supports large volumes of data and are ideal for serving small data-centric applications.

Benefits of Data Marts

With its condensed, application-driven design, a data mart has several benefits to the end-user, among them we can mention:

  • Improved cost-efficiency: There are many factors to consider when setting up a data mart, such as the scope, integrations, and analytical process it seeks to serve. However, a data mart typically only incurs a fraction of the cost of a data warehouse giving the smaller volume of data.
  • Faster and simpler data access: data marts contain a small subset of data, so users can benefit from running queries faster compared with running queries against a complete dataset from a data warehouse.
  • Access to insights: data marts allow for better business intelligence and analytics by leveraging focused data constructed with specific goals and sausages in mind. Teams and units can extract insights faster in shorter periods, benefiting the entire company or enterprise with faster business processes and optimized operations.
  • Simplified maintenance: Data Quality issues can be addressed more quickly in data marts as their specific contents, and smaller volume of data allows for improved maintenance processes. Ultimately this leads to less data clutter.
  • Easier and faster implementation: A data warehouse involves significant implementation time, as it requires aggregating data from several internal and external sources. On the other hand, smaller subsets of data are needed to set up data marts. Therefore, implementation tends to be more efficient and include less set-up time.

Data marts can drive benefits to entire companies and organizations as they improve business intelligence processes, operational optimization and facilitate more intelligent tactical business decision-making.

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Satori provides organizations with a data portal that allows data users to easily get access to data using a user interface or a Slack integration. Data is protected by easy-to-manage security policies such as dynamic masking, and sensitive data is continuously discovered.

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