In the age of digitization and big data, a lot revolves around one thing: data. Terms such as data mart and data lineage regularly catch the eye. It is not always clear exactly what the technical terms are, which is why this article is intended to provide a brief overview.

What is a data mart?

Data marts are a kind of collection point for user-defined data. In doing so, data is extracted from large data stocks and made accessible in isolation for certain user groups. They thus form a sub-segment of a data warehouse and can help to make certain data accessible to the user more quickly and with less effort. This not only saves time but also costs.

Data Mart vs Data Warehouse

Both data marts and data warehouses are used to store and manage data records until they are used. Data warehouses are specialized in organizing the entire data of a company, while data marts only organize collection points for the data of individual departments. They represent a tool that isolates certain data records and makes them available separately to the respective function field.

species

A basic distinction is made between 3 different categories for data marts.

Dependent

Dependent data marts are always directly related to an enterprise data warehouse in that they are developed according to the top-down principle. For this purpose, data is first combined at a collection point and then certain data records are extracted, which are then distributed in their intended data mart.

Independent

An independent data mart, on the other hand, is not linked to a data warehouse and thus forms an autonomous system. Data is obtained from internal and external data sources of an organization instead of from the collection point of the data warehouse and then specifically distributed to the individual data marts. This type of data marts is thus much easier to implement and particularly helpful when pursuing short-term business goals.

Hybrid

Hybrid data marts describe the connection of dependent and independent data marts in a system by obtaining data from a data warehouse as well as from internal and external sources of a company. This allows you to combine the advantages of both methods and create a complex but clear system.

benefits

Because of their function as an accelerator when accessing special data sets, data marts offer many advantages.

  • Minimizing the time it takes to acquire certain data
  • Ready for use much faster than an enterprise data warehouse
  • Data marts require comparatively little specialist knowledge for implementation
  • Inexpensive alternative to an enterprise data warehouse
  • Data marts help improve the performance of a data warehouse because they can obtain data with less effort
  • Thanks to the data mart, KPIs are easier to monitor
  • Data marts support data maintenance by assigning data records to specific departments, which in turn can monitor them independently

What is data lineage?

Data lineage plays a role in connection with the origin of data, which is why the term is often used as a synonym. Data Lineage has the task of recording changes and optimizations of data as well as the development of their elements in a history. It tracks a data record on its journey from creation to adjustments to the final destination and at the same time also documents the associated properties. Simply put, data lineage can be thought of as a kind of biography of a data set.

benefits

With its function, data lineage offers many advantages for the user.

  • Data can be fully monitored at any time
  • Increased transparency about the development and history of data sets
  • The quality of the data is retained
  • Helpful when it comes to confidential data that needs to be protected
  • Companies can use data lineage to more easily comply with data-based standards and regulations

Beyond data mart and data lineage

The following articles also provide more on the subject of data and big data:

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Dr. Dominic Lindner
Author

I blog about the influence of digitalization on our working world. For this purpose, I provide content from science in a practical way and show helpful tips from my everyday professional life. I am an executive in an SME and I wrote my doctoral thesis at the University of Erlangen-Nuremberg at the Chair of IT Management.

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