In today’s landscape, where big data and analytics reign supreme, data marts have emerged as a crucial tool for efficiently transforming vast amounts of information into actionable insights. Unlike Data Warehouses, which are designed to handle massive datasets, data marts focus on making data easily accessible and readily available for Analytics. The rationale is simple: business professionals shouldn’t have to navigate complex queries to retrieve the data necessary for their reports. This is precisely where the strategic implementation of data marts by forward-thinking companies comes into play.

A data mart can either be derived from an existing data warehouse, following the top-down approach, or it can be built using alternative sources, such as internal operational systems or external data.

Essentially, a data mart is a subject-specific database, often representing a partitioned segment of a larger enterprise data warehouse. The data contained within a data mart typically correlates with a specific business unit—be it sales, finance, or marketing. By providing direct access to relevant information from a data warehouse or operational data store, data marts significantly expedite business processes. Accessible within days as opposed to months, these focused datasets enable quick, cost-effective acquisition of valuable insights, tailored to specific business areas.

Related Concept

The term One Big Table is related here, denoting the amalgamation of various tables into a single, large table optimized for a specific business unit.

A dependent data mart lets you combine all your business data into a single data warehouse, giving you the typical benefits of centralization.

The distinction here is particularly intriguing:

Logical view: This represents a virtual table or view that, while logically distinct, remains physically integrated within the data warehouse.Physical subset: In contrast, this entails a data extract that exists as a physically separate database from the data warehouse.

or two more two primary approaches to building dependent data marts (todo: read more):

Direct Access Approach: Here, both the enterprise data warehouses and data marts are constructed in a manner that allows operators to access both as needed.Federated Approach: Alternatively, this approach involves storing the results of the ETL (Extract, Transform, Load) process in a temporary storage area, such as a common data bus, rather than in a physical database. This limits operator access to only departmental data, which can sometimes lead to a “data junkyard” scenario, where data, although originating from a shared source, is largely underutilized or discarded.

In essence, the logical view aligns with concepts such as Semantic Layer or Data Virtualization.

Independent data marts are developed without relying on a central Data Warehouse. They are ideal for smaller units or groups within an organization. These marts operate autonomously, inputting and analyzing data separately from other systems.

The major downside of independent data marts is the increase in data redundancy across the organization. Each independent data store often requires its copy of comprehensive business information, leading to duplicated data. Furthermore, as these data stores directly access files or tables from operational systems, they can significantly limit the scalability of Decision Support Systems (DSS).

Hybrid data marts combine the features of both dependent and independent marts, allowing for the integration of data from various operational source systems in addition to a data warehouse. This type is particularly advantageous for scenarios requiring ad hoc integration, such as incorporating a new group or product line into the business.

Hybrid data marts are versatile and suitable for businesses with multiple databases needing quick data turnaround. They require minimal data cleaning, support large storage structures, and offer the flexibility of merging the benefits of both dependent and independent systems.

Read more on Types of Data Marts: Definition and Implementation.

Origin: RW What Is a Data Mart (Vs a Data Warehouse) Talend
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Created 2022-09-19