What is Data Mart?
A data mart is a subset of a data warehouse created to support a single business function or department inside a company. It is a compact, single-subject data repository that keeps a fraction of a company’s data in a more concentrated and readily accessible manner.
Data marts are often built to satisfy the demands of a certain business unit or department, such as sales, marketing, finance, or operations.
- They provide customers with a consolidated picture of pertinent data from several sources, enabling them to examine the data and get insights into their company operations.
Extraction, transformation, and loading (ETL) procedures, database replication, and data virtualization may all be used to generate data marts. A mart’s data is often arranged to support its particular business purpose and is designed for quick querying and analysis.
Types of Data Mart
- Dependent Data Mart– A dependent data mart is developed directly from a data warehouse. It is intimately connected with the warehouse and depends on its architecture, data formats, and metadata. A dependent data mart is often created for a single business unit or department and offers a pre-configured subset of the data warehouse. Changes in the warehouse’s data are automatically reflected in the dependent mart.
- Independent Data Mart– An independent data mart is a freestanding architecture that is developed independently of the data warehouse. It is tailored to a particular business unit or department and includes a subset of the data warehouse. Developing a standalone mart may be accomplished by the use of ETL procedures. Changes in the warehouse’s data do not immediately update the independent mart.
Dependent marts work in tandem with the data warehouse to maintain data consistency and accuracy. They are, however, more difficult to set up and manage. Independent data marts are simpler to set up and manage than dependent ones but may be less consistent and accurate.
In addition to these two major forms, hybrid marts incorporate characteristics of both dependent and independent types. A hybrid mart is comprised of a portion of the data warehouse as well as data acquired from other sources or developed locally. They are also more adaptable than the dependent ones but less closely connected than the independent ones.
Because ML systems are more fragile than you think. All based on our open-source core.
Open SourceData Warehouse vs. Data Mart
Both data warehouses and data marts are used to store and manage data, although their scope, architecture, and purpose vary.
A data warehouse is a big, centralized repository that contains data from several sources within an organization. It is intended to aid strategic decision-making by offering a complete perspective of the organization’s data. It may also include historical data that can be used to discover trends and patterns over time. Data warehouses are often designed to handle complicated queries and data analysis, and they may use specialist technologies such as data mining and OLAP (online analytical processing).
A data mart, on the other hand, is a more compact, decentralized repository that holds a subset of data from a data warehouse or other sources. It is intended to meet the demands of a certain business unit or department inside an organization, and it may include current or recent data that may be utilized for tactical decision-making.
Advantages of Data Mart
- Improved data quality– They are often constructed using high-quality data from several sources, which may assist an organization’s overall data quality.
- Cheaper costs– Because marts may be established faster and cheaper than a full-scale data warehouse, they are an appealing alternative for small to medium-sized businesses.
- Faster access– Additionally, they give quicker access to relevant data for users in that function since they are built to support a particular business purpose.
- Greater adaptability– Marts may be quickly changed and updated to match an organization’s or business function’s changing demands.
As a whole, data marts may help firms enhance their data analysis skills by presenting a consolidated view of important data for certain business processes or departments.