Data Mart

Within business intelligence, a data mart is the access layer of a data warehouse that is used to provide users with data. Data marts are often seen as small slices of the data warehouse. Data warehouses typically house enterprise-wide data, and information stored in a data mart usually belongs to a specific department or team.

The key objective for data marts is to provide the business user with the data that is most relevant for BI in the shortest possible amount of time. This allows users to develop and follow a train of thought, without needing to wait long periods for queries to complete. Data marts are designed to meet the demands of a specific group and have a comparatively narrow subject area. However, narrow in focus doesn’t necessarily mean small in size. Data marts may contain millions of records and require gigabytes of storage.

What is a Data Mart?

A data mart is a subset of a data warehouse, focused on a specific business line or department. It is designed to provide users with access to a segmented slice of the organization’s data for specific analytical or reporting purposes. Unlike the broad scope of a data warehouse that serves the entire organization, a data mart is tailored to meet the needs of a particular group, such as sales, finance, or marketing, making it more efficient for targeted data analysis and reporting.

Why Create a Data Mart?

Creating a datamart enables businesses to cater to the unique needs of different departments within an organization. It provides a more manageable, focused, and efficient way to access and analyze data relevant to specific business processes or objectives. By segmenting data into marts, organizations can improve performance, enhance data quality, and speed up the delivery of business insights to the stakeholders who need them the most.

The Difference Between Data Marts, Data Lakes, and Data Warehouses

Data marts, data lakes, and data warehouses are designed to handle different usecases.

Data Marts

Subsets of data warehouses, focused on specific business lines or departments, designed to facilitate more specialized analysis.

Data Warehouses

Centralized repositories that aggregate data from multiple sources to provide a unified environment for data analysis and reporting across the entire organization.

Data Lakes

Vast pools of raw data stored in their native format, designed to handle a wide variety of data types, including structured, semi-structured, and unstructured data, ideal for big data and complex analytics projects due to their scalability and flexibility.

Remember, important difference between, data warehouse and data lake, is a data lake stores vast amounts of data in raw form, that doesn’t have it’s structure predefined.

Characteristics of Data Marts

Data marts possess several distinct characteristics that set them apart from other data storage solutions like data warehouses and data lakes. These features make data marts an essential component of an organization’s data strategy, particularly for targeted, department-specific data analysis and reporting. Below are key characteristics of data marts:

1. Subject-Oriented: Data marts are designed around specific business subjects or departments, such as sales, finance, or marketing. This focus allows for a more detailed and relevant analysis within a particular domain.
2. Scoped Data Selection: Unlike data warehouses that aim for a comprehensive collection of an organization’s data, data marts contain only a subset of data. This data is selected based on its relevance to the specific business function or requirement the data mart serves.
3. Simplified Access and Analysis: The targeted nature of data marts simplifies data access and analysis for end-users. By providing a narrower data set, data marts reduce complexity and improve the speed of data retrieval, making it easier for business analysts and other non-technical users to extract insights.
4. Faster Query Performance: Because data marts hold less data than a full data warehouse, query performance is generally faster. This efficiency is particularly valuable for specialized analyses that require quick turnaround times.
5. Independence: Data marts can be developed independently of each other and of a central data warehouse, allowing departments to create and manage their own data marts as needed. This autonomy supports agile development and tailored analytical environments.
6. Lower Cost and Complexity: Compared to the broader scope and scale of data warehouses, data marts are less costly and complex to implement and maintain. Their smaller scale allows for more manageable data volumes and simpler data models.
7. Flexibility: Data marts offer flexibility in terms of design and scalability. They can start small and expand as needed, accommodating growing data needs within a specific business area without affecting the entire organizational data infrastructure.

Types of Data Marts

Data marts can be categorized into different types based on their source of data, method of creation, and the specific use case they serve. Understanding these types helps organizations choose the most suitable approach to meet their analytical and business requirements. The primary types of data marts include:

Independent Data Marts

These data marts are developed separately from any data warehouse and typically focus on the needs of a specific business unit or department. Independent data marts source their data directly from internal or external data sources and operate independently of other data management systems. This independence can lead to faster deployment and flexibility in meeting specific departmental needs but may also result in data silos and inconsistency across the organization.

Dependent Data Marts

Dependent data marts are directly sourced from an existing data warehouse. This type ensures consistency in data definitions and structures across the organization since all data marts and the central data warehouse align. Dependent data marts benefit from the data warehouse’s integration, transformation, and cleansing processes, ensuring high-quality and reliable data for analysis. However, their implementation might be slower and more complex due to the dependency on the central warehouse infrastructure.

Hybrid Data Marts

Hybrid data marts combine aspects of both independent and dependent data marts. They primarily source data from a central data warehouse but can also integrate data from additional sources as needed. This type offers a balance between flexibility and data consistency, allowing departments to address unique analytical needs while maintaining alignment with the organization’s overall data strategy.

Virtual Data Marts

Virtual data marts do not store data physically; instead, they use views or queries to access and present data from a data warehouse or other sources in real-time. This approach minimizes data redundancy and storage requirements. Virtual data marts provide the most up-to-date data, as they directly query the source systems. However, they might impact performance due to the reliance on live data access.

Each type of data mart has its advantages and considerations, making them suitable for different organizational needs. Independent data marts offer quick, tailored solutions for specific departments, while dependent data marts ensure data consistency and reliability across the enterprise. Hybrid data marts provide a flexible approach, leveraging both centralized and departmental data sources, and virtual data marts offer a real-time, efficient solution with minimal storage needs. Selecting the right type of data mart depends on the organization’s specific goals, data architecture, and analytical requirements.

Advantages of using a data mart

• Improves end-user response time by allowing users to have access to the specific type of data they need
• A condensed and more focused version of a data warehouse
• Each is dedicated to a specific unit or function
• Lower cost than implementing a full data warehouse
• Holds detailed information
• Contains only essential business information and data and is less cluttered
• Works to integrate all data sources

The creation and use of a data mart leads to a great summarization of data. A much broader range of data is available with data warehouses; however, this data is generally not summarized, can make it difficult to sort through masses of data, and increases query times. Data marts play a pivotal role in the modern data ecosystem, providing tailored solutions that enhance data accessibility, analysis, and decision-making across different departments within an organization. By understanding the strategic importance of data marts, businesses can leverage them to gain competitive advantages and drive more informed business strategies.