Table of Contents
What Is a Data Mart?
A data mart is a subject-oriented database designed to make specific organizational data easy to find and readily available. A data mart is a condensed version of a data warehouse, which stores all data generated by departments of an organization. With data mart, users can quickly access relevant data and gain insights without searching through an entire data warehouse. The data held in a data mart is often controlled by a single department in an organization, like sales, finance, or marketing. Since Data marts draw data from only a few sources, they allow users to access operational data in a data warehouse within days, thus accelerating business processes. They provide cost-effective ways to gain quick, actionable insights.
Data Mart vs. Data Warehouse
Data Marts and Data Warehouses are highly structured data repositories, but they differ in the scope of data stored and serve different purposes within an organization.
The Data warehouse serves as the central repository of data for the entire organization. At the same time, data mart focuses on data important to and needed by a specific division or line of business. It aggregates data from different sources to support data mining, artificial intelligence, machine learning, which results in improved analytics and business intelligence. Since data warehouse stores all data generated by an organization, access to the warehouse should be strictly controlled. It can be extremely difficult to query data needed for a particular purpose from the enormous pool of data contained in a data warehouse. That is where the data mart is helpful. The main purpose of a data mart is to seperate or partition a subset of the entire dataset to provide easy access to data to end-users.
Both data warehouse and data mart are relational databases built to store transactional data (e.g., numerical order, time value, object reference) in tabular form for ease of organizing and access.
A single data mart can be created from an existing data warehouse in the top-down development approach or from other sources like internal operational systems or external data. The designing process involves several tools and technologies to construct a physical database, populate it with data and implement strict access and management rules. It is a complex process, but the mart enables a business to get more focused insights in less time than working with a broader dataset in a warehouse.
Benefits of a Data Mart
Data Marts are built to enable business users to access the most relevant data in the shortest time. With its small size and focused design, data mart offers several benefits to the end-user, including:
- Contains data that is valuable to specific groups within an organization
- Cost-effective to build than a data warehouse.
- Allows simplified data access. Data marts contain a small subset of data, so users can easily retrieve data as needed compared to sifting through broader data set from a data warehouse.
- Quick access to data insights. Insights gained from a data mart impacts decisions at the department level. Teams can use these focused insights with specific goals in mind, resulting in faster business processes and higher productivity.
- Data mart needs less Implementation Time compared to data warehouse because you only need to focus on a small subset of data. Implementation tends to be more efficient and less time-consuming.
- It contains historical data, which helps data analysts to predict data trends.
- Dependent: Dependent data marts are created by drawing data directly from operational, external or both sources.
- Independent: Independent data mart is created without the use of a central data warehouse.
- Hybrid: This type of data marts can take data from data warehouses or operational systems.
A dependent data mart allows sourcing organization’s data from a single Data Warehouse. It is one of the data mart example which offers the benefit of centralization. If you need to develop one or more physical data marts, then you need to configure them as dependent data marts.
Dependent Data Mart in data warehouse can be built in two different ways. Either where a user can access both the data mart and data warehouse, depending on need, or where access is limited only to the data mart. The second approach is not optimal as it produces sometimes referred to as a data junkyard. In the data junkyard, all data begins with a common source, but they are scrapped, and mostly junked.
An independent data mart is created without the use of central Data warehouse. This kind of Data Mart is an ideal option for smaller groups within an organization.
An independent data mart has neither a relationship with the enterprise data warehouse nor with any other data mart. In Independent data mart, the data is input separately, and its analyses are also performed autonomously.
Implementation of independent data marts is antithetical to the motivation for building a data warehouse. First of all, you need a consistent, centralized store of enterprise data which can be analyzed by multiple users with different interests who want widely varying information.
A hybrid data mart combines input from sources apart from Data warehouse. This could be helpful when you want ad-hoc integration, like after a new group or product is added to the organization.
It is the best data mart example suited for multiple database environments and fast implementation turnaround for any organization. It also requires least data cleansing effort. Hybrid Data mart also supports large storage structures, and it is best suited for flexible for smaller data-centric applications.
Structure of a Data Mart
A data mart and a data warehouse can be organized using a star, vault, snowflake, or other schema as a blueprint.
Usually, a star schema is used that consists of one or many fact tables, referencing dimensional tables in a relational database. In a star schema, fewer joints are required for writing queries.
In the snowflake schema, there’s no clear definition of dimensions. They are normalized, so data redundancy gets reduced, and data integrity is protected. The structure is complicated and difficult to maintain, though it takes less space to store dimension tables.
Data Mart and Cloud Architecture
Businesses are increasingly moving to cloud-based data marts and data warehouses instead of traditional on-premises setups. Business and IT teams are striving to become more agile and data-driven to improve regular decision-making. The benefits of cloud architecture include:
- Decreases need to purchase physical hardware
- Decreases need for manual intervention
- Faster and cheaper to set up and implement cloud data marts
- The cloud-based architecture uses massively parallel processing; hence, data marts can perform complex analytical queries much faster.
The Future of Data Marts Is in the Cloud
Leading cloud service providers provide a shared cloud-based platform to create and store data, access, and analyze efficiently. Business teams can quickly combine transient data clusters for short-term analysis or long-lived clusters for sustained work. With the use of modern technologies, data storage can be easily separated from computing, allowing for extensive scalability for querying data.
Key advantages of cloud-based data marts are:
- Flexible architecture
- Single depository housing all data marts
- On-demand consumption of Resources
- Real-time access to Information
- Higher Efficiency
- Interactive Analytics in Realtime
- Consolidation of Resources that cost less
The main difference between Data warehouse and Data mart is that Data Warehouse is the type of database which is data-oriented in nature, while Data Mart is the type of database which is the project-oriented in nature. The other difference between these two the Data warehouse and the Data mart is that Data warehouse is large in scope whereas Data mart is limited in scope.
Difference between Data warehouse and Data mart
|S.NO||Data Warehouse||Data Mart|
|1.||Data warehouse is a Centralized system.||While it is a decentralized system.|
|2.||In data warehouse, lightly denormalization takes place.||While in Data mart, highly denormalization takes place.|
|3.||Data warehouse is top-down model.||While it is a bottom-up model.|
|4.||To build a warehouse is difficult.||While to build a mart is easy.|
|5.||In data warehouse, Fact constellation schema is used.||While in this, Star schema and snowflake schema are used.|
|6.||Data Warehouse is flexible.||While it is not flexible.|
|7.||Data Warehouse is the data-oriented in nature.||While it is the project-oriented in nature.|
|8.||Data Warehouse has long life.||While data-mart has short life than warehouse.|
|9.||In Data Warehouse, Data are contained in detail form.||While in this, data are contained in summarized form.|
|10.||Data Warehouse is vast in size.||While data mart is smaller than warehouse.|
|11.||It collects data from various data sources.||It generally stores data from a data warehouse.|
|12.||Long time for processing the data because of large data.||Less time for processing the data because of handling only a small amount of data.|
|13.||Complicated design process of creating schemas and views.||Easy design process of creating schemas and views.|
Scope of the data mart
The scope of the data mart defines the boundaries of the project and is typically expressed in some combination of geography, organization and application, or business functions. Defining scope usually requires making compromises as you try to balance resources (such as people, systems, and budget) with the scheduled completion date and the capabilities you promised to deliver. Defining your scope and making it clear to everyone involved is important because it:
- Sets the right expectations
- Prioritizes incremental development
- Highlights risks and issues
- Allows you to estimate costs
- A Data Mart is defined as a subset of Data Warehouse that is focused on a single functional area of an organization.
- Data Mart helps to enhance user’s response time due to a reduction in the volume of data.
- Three types of data mart are 1) Dependent 2) Independent 3) Hybrid
- Important implementation steps of Data Mart are 1) Designing 2) Constructing 3 Populating 4) Accessing and 5) Managing
- The implementation cycle of a Data Mart should be measured in short periods of time, i.e., in weeks instead of months or years.
- Data mart is cost-effective alternatives to a data warehouse, which can take high costs to build.
- Data Mart cannot provide company-wide data analysis as data set is limited.
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