Data Mart

Understanding Data Mart

A data mart is a specialized, streamlined portion of a data warehouse, designed to accommodate a single department or business function within an enterprise. It's a compact, subject-specific data storehouse, containing a selected portion of the business's data in an easily accessible and more focused manner.

Data marts are typically constructed to cater to the unique requirements of a specific business segment or department such as finance, sales, marketing, or operations. They present customers with an integrated snapshot of relevant data from various origins, empowering them to scrutinize the data and extract insightful details about company operations.

The creation of data marts may utilize extraction, transformation, and loading (ETL) procedures, database replication, and data virtualization methods. The setup of a mart's data is often tailored to enhance its distinct business purpose and facilitate expedited query and analysis.

Data Mart Types

  • Dependent Data Mart - A dependent data mart is derived directly from a data warehouse. Its setup, data types, and metadata are intricately tied to the warehouse, for which it often provides a pre-configured segment. Alterations in the warehouse data are automatically mirrored in the dependent mart.
  • Independent Data Mart - An independent data mart is an autonomous structure, conceived and developed devoid of a data warehouse. Adapted to a specific business division, it, too, represents a segment of the data warehouse. Establishing such a standalone mart generally involves ETL processes. Changes in the warehouse data, however, are not immediately replicated in the independent mart.

While dependent marts ensure consistent and accurate data by working alongside the data warehouse, they demand a more complex setup and handling. Independent data marts, easier to configure and manage, may compromise data integrity and precision.

Hybrid marts, which blend the characteristics of both dependent and independent data marts, encompass a part of the data warehouse and locally created data or data from other sources. They offer higher flexibility than dependent marts, but maintain less connectivity than independent marts.

Data Warehouse vs. Data Mart

Data warehouses and data marts both serve data storage and management, but their scale, structure, and function vary.

A data warehouse is a large-scale, centralized storehouse comprising data from numerous internal sources. Aimed at facilitating strategic decision-making, it provides a comprehensive view of the organization's data, often inclusive of historical data for trend and pattern analysis over time. Designed for intensive queries and data analysis, data warehouses resort to advanced technologies like data mining and OLAP (Online Analytical Processing).

Contrastingly, a data mart is a smaller, decentralized data repository, housing a portion of data from a data warehouse or other origins. It primarily addresses the needs of a specific department or business unit within an enterprise, and may contain current or historical data for tactical decision-making purposes.

Benefits of Data Mart

  • Enhanced data quality - Constructed with top-grade data from multiple sources, marts can help improve an enterprise's overall data quality.
  • Reduced costs - As marts can be constituted more swiftly and economically than a full-blown data warehouse, they are a viable option for small to mid-sized businesses.
  • Quick access - Tailored to a particular business function, marts deliver speedy access to pertinent data to users in that function.
  • Increased adaptability - Marts can be promptly modified and updated to sync with evolving demands of an organization or a particular business function.

Overall, data marts can empower companies to boost their data analysis capabilities by offering a consolidated view of critical data pertaining to specific departments or business processes.

Integrate | Scan | Test | Automate

Detect hidden vulnerabilities in ML models, from tabular to LLMs, before moving to production.