Data has become the focal point of every industry in today's digital age. We consumed over 64.2 zettabytes worth of data in 2020 alone is enough evidence to support the statement. Data's role in the modern market is only surging as it is expected to reach 180 zettabytes by 2025.
That is why tools like a data mart have become a must for businesses. It is a subset of directorial information, usually steered towards a primary data subject or specific purpose. An effective data mart can help businesses analyze, assess, and store records effectively, saving valuable time and resources.
However, despite Data mart's prevalent use in BI or Business Intelligence, many businesses still ask what is data mart. Learning the answer to the question is pivotal as it can define your organization's future. That is why here is a complete guide to the data mart, including its types, data mart examples, and more.
As stated already, a data mart is a database based on subjects, which is generally a partitioned section of a business data warehouse. The data subset aligns with particular business units, such as finance, marketing, or sales.
A well-organized data mart can help businesses gain relevant information promptly. It enables a cost-effective channel where enterprises can access actionable insights in days instead of weeks or even months.
Even businesses that have accessed data marts confuse it with a data warehouse. Unlike a data mart, a data warehouse is a centralized hub that gathers data from multiple sources. As the name suggests, it holds a huge amount of data within a complex mechanism.
Due to this property, building and maintaining a data warehouse is significantly high. Moreover, as a centralized data facility, it keeps the information standardized. It means that a Data Warehouse records and converts the data to simplify its administration.
On the other hand, a data mart comprises specific data required for particular functions. Therefore, it is relatively compact, quick, and requires lower resources for maintenance. Generally, businesses create Data marts from an existing warehouse. It is referred to as the top-down approach, which is a common practice nowadays.
Similarly, different business units can also create customized data marts based on their requirements. Finally, different data marts can be joined to build a single data warehouse. The process is referred to as the bottom-up development approach.
Given the subtle differences between the concepts, many candidates opt for a Data Science course to grasp an extensive overview. Here is a simplified table that can help you understand the differences better:
|Data Mart||Data Warehouse|
|Size||Under 100 GB||Over 100 GB|
|Subject||One Subject||Multiple Subjects|
|Data Sources||Multiple Sources||Many Source Systems|
|Data Integration||Singular Subject Area||All the Business Data|
|Time to Build||Minutes, Weeks, Months||Many Months to Years|
Now that you understand data mart meaning, it is time to learn about its types.
Dependent Data Marts
Deepened Data marts are the most common type of marts. These are linked to a Data Warehouse, allowing businesses to derive data. It facilitates a system where businesses can create a centralized and simplified system.
This type of mart follows the top-down approach, which many learn during their Data Science training. Depended data mart requires a specific data set from the warehouse, which is restructured and loaded to the mart for the query. It can be further sub-categorized into two sections:
The type of Data Mart uses granular data (lowest data level) within the warehouse as the reference for every dependent data mart.
Independent Data Marts
Unlike dependent data marts, it acts as a standalone system within the warehouse. It focuses on a singular business function or subject area. It extracts data from external and/or internal sources, processes it, and loads it to the main repository.
The data is stored within the mart until the business needs it for insights and analytics. These are not difficult to develop or design while they help businesses achieve short-term goals. However, enterprises may find it hard to manage them despite their simplicity.
That is why many candidates prefer completing a Data Science certification to master the concepts.
Hybrid Data Marts
As the name suggests, a hybrid data mart merges data from a pre-existent warehouse and different source systems. It unifies focus and the speed of the top-down approach with the enterprise-grade incorporation of the bottom-up approach.
Given its complexity, many candidates choose a Data Science Certification Course based on the topic's inclusion.
Businesses create data marts for numerous reasons as they render multiple use-cases. However, here is an overview of the most prevalent benefits:
Prompt Access: Data marts present a time-efficient solution for reaching a specific data set for BI (Business Intelligence).
Great Data Warehouse Alternative: While it is not identical, a data mart can be an alternative for a data warehouse.
Improves Data Warehouse Performance: Besides being a viable alternative, a data mart can also elevate the application of a data warehouse. With a hybrid or even dependent data mart, businesses can take off some burned off the data warehouse.
Simplistic Setup: A data mart is a relatively simple setup compared to a data warehouse or other data sources.
Data Segregation: Businesses can use data marts to divide and record data depending on their needs for better mobility.
Data marts bode well with numerous business needs as an effective data source. That is why many modern businesses have opted for the method. They are simple to set, easy to use, and produce results.
However, despite such perks, many businesses still do not know what is data mart. The article has shed some light on the concept, its types, use-cases, and more. Go through the article to understand its functionalities and improve your business insights.
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