What is a data mart?

Data marts make specific data available to a defined group of users, which allows those users to quickly access critical insights without wasting time searching through an entire data warehouse. For example, many companies may have a data mart that aligns with a specific department in the business, such as finance, sales or marketing.

Data marts, data warehouses, and data lakes are crucial central data repositories, but they serve different needs within an organization.

A data warehouse is a system that aggregates data from multiple sources into a single, central, consistent data store to support data mining, artificial intelligence (AI), and machine learning—which, ultimately, can enhance sophisticated analytics and business intelligence. Through this strategic collection process, data warehouse solutions consolidate data from the different sources to make it available in one unified form.

A data mart (as noted above) is a focused version of a data warehouse that contains a smaller subset of data important to and needed by a single team or a select group of users within an organization. A data mart is built from an existing data warehouse (or other data sources) through a complex procedure that involves multiple technologies and tools to design and construct a physical database, populate it with data, and set up intricate access and management protocols.

While it is a challenging process, it enables a business line to discover more-focused insights quicker than working with a broader data warehouse data set. For example, marketing teams may benefit from creating a data mart from an existing warehouse, as its activities are usually performed independently from the rest of the business. Therefore, the team doesn’t need access to all enterprise data.

A data lake, too, is a repository for data. A data lake provides massive storage of unstructured or raw data fed via multiple sources, but the information has not yet been processed or prepared for analysis. As a result of being able to store data in a raw format, data lakes are more accessible and cost-effective than data warehouses. There is no need to clean and process data before ingesting.

For example, governments can use technology to track data on traffic behavior, power usage, and waterways, and store it in a data lake while they figure out how to use the data to create “smarter cities” with more efficient services.

Data marts are designed to meet the needs of specific groups by having a comparatively narrow subject of data. And while a data mart can still contain millions of records, its objective is to provide business users with the most relevant data in the shortest amount of time.

With its smaller, focused design, a data mart has several benefits to the end user, including the following:

• Cost-efficiency: There are many factors to consider when setting up a data mart, such as the scope, integrations, and the process to extract, transform, and load (ETL). However, a data mart typically only incurs a fraction of the cost of a data warehouse.
  
  
• Simplified data access: Data marts only hold a small subset of data, so users can quickly retrieve the data they need with less work than they could when working with a broader data set from a data warehouse.
  
  
• Quicker access to insights: Intuition gained from a data warehouse supports strategic decision-making at the enterprise level, which impacts the entire business. A data mart fuels business intelligence and analytics that guide decisions at the department level. Teams can leverage focused data insights with their specific goals in mind. As teams identify and extract valuable data in a shorter space of time, the enterprise benefits from accelerated business processes and higher productivity.
  
  
• Simpler data maintenance: A data warehouse holds a wealth of business information, with scope for multiple lines of business. Data marts focus on a single line, housing under 100GB, which leads to less clutter and easier maintenance.
  
  
• Easier and faster implementation: A data warehouse involves significant implementation time, especially in a large enterprise, as it collects data from a host of internal and external sources. On the other hand, you only need a small subset of data when setting up a data mart, so implementation tends to be more efficient and include less set-up time.

There are three types of data marts that differ based on their relationship to the data warehouse and the respective data sources of each system.

• Dependent data marts are partitioned segments within an enterprise data warehouse. This top-down approach begins with the storage of all business data in one central location. The newly created data marts extract a defined subset of the primary data whenever required for analysis.
  
  
• Independent data marts act as a standalone system that doesn't rely on a data warehouse. Analysts can extract data on a particular subject or business process from internal or external data sources, process it, and then store it in a data mart repository until the team needs it.
  
  
• Hybrid data marts combine data from existing data warehouses and other operational sources. This unified approach leverages the speed and user-friendly interface of a top-down approach and also offers the enterprise-level integration of the independent method.

A data mart is a subject-oriented relational database that stores transactional data in rows and columns, which makes it easy to access, organize, and understand. As it contains historical data, this structure makes it easier for an analyst to determine data trends. Typical data fields include numerical order, time value, and references to one or more objects.

Companies organize data marts in a multidimensional schema as a blueprint to address the needs of the people using the databases for analytical tasks. The three main types of schema are star, snowflake, and vault.

Star schema is a logical formation of tables in a multidimensional database that resembles a star shape. In this blueprint, one fact table—a metric set that relates to a specific business event or process—resides at the center of the star, surrounded by several associated dimension tables.

There is no dependency between dimension tables, so a star schema requires fewer joins when writing queries. This structure makes querying easier, so star schemas are highly efficient for analysts who want to access and navigate large data sets.

A snowflake schema is a logical extension of a star schema, building out the blueprint with additional dimension tables. The dimension tables are normalized to protect data integrity and minimize data redundancy.

While this method requires less space to store dimension tables, it is a complex structure that can be difficult to maintain. The main benefit of using snowflake schema is the low demand for disk space, but the caveat is a negative impact on performance due to the additional tables.

Data vault is a modern database modeling technique that enables IT professionals to design agile enterprise data warehouses. This approach enforces a layered structure and has been developed specifically to combat issues with agility, flexibility, and scalability that arise when using the other schema models.

Data vault eliminates star schema's need for cleansing and streamlines the addition of new data sources without any disruption to existing schema.

Data marts guide important business decisions at a departmental level. For example, a marketing team may use data marts to analyze consumer behaviors, while sales staff could use data marts to compile quarterly sales reports. As these tasks happen within their respective departments, the teams don't need access to all enterprise data.

Typically, a data mart is created and managed by the specific business department that intends to use it. The process for designing a data mart usually comprises the following steps:

1. Document essential requirements to understand the business and technical needs of the data mart.
   
   
2. Identify the data sources your data mart will rely on for information.
   
   
3. Determine the data subset, whether it is all information on a topic or specific fields at a more granular level.
   
   
4. Design the logical layout for the data mart by picking a schema that correlates with the larger data warehouse.

With the groundwork done, you can get the most value from a data mart by using specialist business intelligence tools, such as Qlik or SiSense. These solutions include a dashboard and visualizations that make it easy to discern insights from the data, which ultimately leads to smarter decisions that benefit the company.

While data marts offer businesses the benefits of greater efficiency and flexibility, the unstoppable growth of data poses a problem for companies that continue to use an on-premises solution.

As data warehouses move to the cloud, data marts will follow. By consolidating data resources into a single repository that contains all data marts, businesses can reduce costs and ensure all departments have unfettered access to data they need in real-time.

Cloud-based platforms make it possible to create, share, and store massive data sets with ease, paving the way for more efficient and effective data access and analysis. Cloud systems are built for sustainable business growth, with many modern Software-as-a Service (SaaS) providers separating data storage from computing to improve scalability when querying data.