Data mart vs Data Warehouse: Differences - 【2020】
data mart vs data warehouse

Data mart vs Data Warehouse: Differences

Companies rely on the data warehouse for accurate business intelligence. They serve as a central repository and store existing and historical data for analysis and data-driven business decisions. But so do data marts. What is the difference between these two data repositories?

In this blog you will find the answer to the question Data Mart vs. Data Warehouse. What is a data warehouse and how is it different from a data mart?

A data warehouse refers to a structure that consolidates data from multiple source systems. The main purpose of a data warehouse is to provide a correlation between data from different systems, for example, product information stored in one system and order data stored in another system.

A data warehouse is used for online analysis processing (OLAP), where complex queries are executed for analysis instead of processing transactions. It is an essential element of Business Intelligence, as it stores large amounts of data in a single location to gain important insights and streamline business processes. In this way, it supports the decision making process of companies.

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Contents

Data Mart – The Basics

A data mart refers to a structure that is specific to data warehousing configurations. It is a subset of a data warehouse. This is typically used to access customer-related information. Therefore, a data mart typically focuses on one division or team.

Unlike a data warehouse implementation, which can extend over several months or even years, a data mart is typically implemented in a few months. This is due to its smaller size (less than 100 GB) and the fact that data is drawn from fewer sources.

A data mart is preferred for departmental analysis and reporting activities, such as sales, marketing, finance, etc., as these activities are typically performed in a separate business unit. Therefore, BI does not require enterprise-wide data. For example, a marketing specialist can use a dedicated data marketplace to perform market analysis and reporting.

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Depending on their needs, companies can use several data marts for different departments and merge them to create a single data warehouse later. This approach is called the Kimball Dimensional Design Method. Another method, known as the Inmon approach, is to first design a data warehouse and then create multiple data marts for specific departments as needed.

For reasons of time and budget, companies often choose the Kimball approach.

Let’s take a look at how these two data warehouses differ.

Data Mart vs. Data Warehouse: a comparison

The main differences between the two structures are summarized here:

Data Warehouse

  • A data warehouse stores data from numerous subject areas.
  • It acts as a central data repository for a company.
  • A data warehouse is designed using constellation schemes of stars, snowflakes, galaxies or facts. However, the star pattern is the most widely used.
  • Designing and using a data store is difficult because it usually contains more than 100 GB of data.
  • A data warehouse is intended to support the decision-making process in a company. Therefore, it provides an enterprise-wide understanding of a centralized system and its autonomy.
  • A data warehouse stores detailed information in unstandardized or standardized form.
  • A data warehouse is large and integrates data from a large number of sources that can cause the risk of failure.
  • A data warehouse is a thematic and temporal variant in which data exists longer.
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Data Mart

  • A data mart contains data related to a department, e.g. HR, finance, marketing, etc.
  • This is a logical subsection of a data warehouse where data is stored on inexpensive servers for certain departmental applications.
  • A data mart uses a star schema to design tables.
  • Designing and using a data mart is comparatively easy due to its small size (less than 100 GB).
  • A data mart is designed for specific user groups or company departments. Therefore, it offers departmental interpretation and decentralized data storage.
  • A data mart contains highly denormalized data in summary form.
  • A data mart has smaller dimensions and data is integrated from fewer sources, so the risk of failure is lower.
  • A data mart is intended for certain business areas and stores data for a shorter period.

The baseline

In a data warehouse, the operator is offered an integrated platform where decision support queries can be easily made. On the other hand, a data mart provides a departmental interpretation of the stored data.

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For example, a specialist in your finance department can use a financial data center for financial reporting. However, if your company wants to expand, a data mart is required because you need to integrate data from multiple sources across the company to make an informed decision.

The ideal data warehouse for an organization is one that meets the needs of the business.

Data Mart FAQS

What is Data Mart?

The Data Mart is a small database (compared to the whole set) where we will find data from specific areas. You can find in a Data Mart a compendium of data related to the area of sales, collection, warehouses, etc.

Types of Data Marts

  • Dependent
  • Independent
  • Hybrid