Data Marts and Data Warehouses: What are they, and how do they differ?

Companies have several options regarding technologies to build a data analytics stack, such as data mart and data warehouse.

A centralized data warehouse, a collection of more specialized data marts, or a hybrid of the two may be considered by data managers. Though data warehouses and data marts are similar, they serve diverse purposes, and a company may choose to employ one or both for various purposes.

Another option is a data lake, which lacks the schema-based organization of a data warehouse or data mart.

An Overview of Data Warehouses

A data warehouse is a structure that collects data from many sources and consolidates it. A centralized data warehouse’s primary function is to correlate data from several source systems, such as product information kept in one system and purchase order data maintained in another.

Data warehouses may be utilized in a variety of business situations. Endowments, account balances, accounting transaction information, and so on are examples of data warehouses for the finance department.

A data warehouse is employed for online analytical processing (OLAP), which requires complicated queries to evaluate transactions.

It’s a significant part of business intelligence since it keeps a lot of data in one place, which can subsequently be utilized to extract critical insights and streamline company operations. As an outcome, it aids in the decision-making process of businesses.

When choosing a data warehouse solution, weighing the pros and cons of various solutions on the market is necessary.

RELATED: Data Warehouses vs Data Lakes: What are the Differences?

What Exactly is a Data Mart?

Generally used to retrieve helpful content to customers, a data mart is a subset of a data warehouse. It’s a framework that is unique to data warehousing environments.

Consequently, a data mart is usually centered on a single business line or team and pulls data from a single source.

A data mart’s subset of data is usually associated with a particular business unit, such as sales, finance, or marketing. Data marts speed up business operations by allowing users to access relevant data in days rather than months or years from a data warehouse or operational data store.

A data mart is a cost-effective approach to quickly getting meaningful insights since it only contains data relevant to a particular business sector.

A Data Mart’s Structure

A data mart can be structured using a star, snowflake, vault, or other structure as a blueprint, comparable to a data warehouse.

In a relational database, IT teams generally employ a star schema, consisting of one or even more fact tables, each with a set of metrics about a particular business process or event, and dimension tables, each with a unique identifier linked to a fact table.

Data Mart Types

There are three types of data marts, namely independent, hybrid, and dependent. They are classified according to their relationship to the data warehouse and the data sources utilized to build the system.

• Independent Data Mart: A standalone data mart is a system constructed without a data warehouse that concentrates on a single topic area or business function. Data is retrieved from data sources (internal, external, or both), processed, and fed into the repository of the data mart, where it is stored for business analytics.

While simple to create and build, each Independent data mart may become challenging to manage as business demands develop and become more sophisticated. However, they help achieve short-term objectives.

• Dependent Data Mart: The creation of a dependent data mart from an existing corporate data warehouse. The top-down strategy begins with storing all company data in a centralized location, then extracting a well-defined subset of the data for analysis.

A particular set of data is collected (formed into a cluster) from the warehouse, reorganized, and fed into the data mart, where it can be searched, to create a data warehouse. It might be either a logical or physical subset of the data warehouse:

1. 1. Logical view – A virtual table or view separated from the data warehouse intellectually but not physically
   2. Physical subset – Extraction of physically distinct data from the existing corporate data warehouse

For all dependent data marts, Granular data, the lowest level of data in the target set, serves as the single point of reference.

• Hybrid Data Mart: A hybrid data mart combines information from an existing data warehouse with information from other operational source systems. It combines the speed and end-user-centricity of a top-down approach with the enterprise-level integration advantages of a bottom-up strategy.

Data Mart vs. Data Warehouse

Both data marts and data warehouses are highly organized repositories for storing and managing data until it is required.

However, the breadth of data stored differs: data warehouses are designed to serve as the primary data repository for the whole company, whereas a data mart fits the needs of a single division or business function.

Because a data warehouse includes information about the entire organization, it’s ideal to limit who has access to it.

Furthermore, searching for the data you want in a data warehouse is challenging for the organization. Consequently, the fundamental goal of a data mart is to isolate or partition a smaller collection of data from a more extensive set to make it more accessible to users at the end.

A data mart can be developed from a top-down method from an existing data warehouse or through other sources, such as internal operating systems or external data.

It’s a relational database that contains transactional data (time value, numerical order, reference to one or more objects) in columns and rows, making it easy to organize and retrieve. It’s similar to a data warehouse.

However, separate business units may construct their data marts depending on their data requirements. Multiple data marts can be combined to produce a single data warehouse if a business needs demand. This is the bottom-up method of development.

Data Warehouse vs. Data Mart – The Differences

The primary distinction between a data warehouse and a data mart is that a data warehouse is a data-oriented database. A data mart is a project-oriented database.

Another difference between the data warehouse and the data mart is that the data warehouse has a far broader reach.

1. The data warehousing facility has a lengthy lifespan. On the other hand, data marts have a shorter lifespan than warehouses.
2. The essence of a data warehouse is data-oriented. A data mart is, nevertheless, project-oriented in nature.
3. The data warehouse follows a top-down approach. A data mart, though, is a bottom-up approach.
4. The fact constellation schema is used in data warehouses. Star schema and snowflake schema are used in the mart.
5. The data warehouse is adaptable. A data mart is not, however, adaptable.
6. Data are stored in detail in a data warehouse, while data is presented in a summary format in a data mart.
7. The size of the data warehouse is enormous. However, the data mart is smaller compared to a data warehouse.
8. It is tough to construct a warehouse, while a mart is comparatively simple to build.
9. Denormalization is done minimally in a data warehouse. In a data mart, there is a lot of denormalization.

Key Differences

• More minor, subject-specific chunks of data are taken from a data warehouse as data marts.
• Data marts are a collection of vital information for a particular segment. Only a few people have full access to the data warehouse.
• Because data marts are smaller subsets of the data warehouse, they require less overhead and can analyze data quicker.
• A data warehouse is much bigger, often a terabyte or more, but a data mart is typically less than 100 GB.
• A data warehouse contains an organization’s cleansed, normalized data across all its business units. In contrast, a data mart has a narrower scope, generally focused on a single line of business.
• The data for a data warehouse comes from databases, whereas the data for a data mart comes from the data warehouse.

The Architecture of a Data Warehouse and a Data Mart

This explanation illustrates how the data warehouse and data mart interact. Each of the databases is a separate transactional source. The data is prepared for sending to the operational data storage via an ETL procedure (ODS).

The ODS processes the data warehouse’s data. Subject-specific, restricted data sets are delivered to the various data marts from the data warehouse. Furthermore, reports and dashboards are generated from the data marts. Reports and dashboards may often be generated straight from the data warehouse.

Conclusion

Data warehouses provide an organizational perspective, a single centralized storage system, intrinsic design, and application independence, whereas data marts provide a departmental view and decentralized collection

Here are some of the advantages that data marts offer businesses:

• Efficient access: Data marts allow different departments to own and control their data. They are also time-saving when it comes to accessing a specific set of data for business intelligence needs.
• Inexpensive data warehouse alternative: Where required data sets are smaller, data marts are an affordable alternative. Their straightforward design means companies need fewer technical resources to set up, with most independent data mart up and running in a week.

• Improve data warehouse performance: Dependent and hybrid data marts can help improve the performance of the corporate data warehouse by taking on the burden of processing. If placed in a separate processing facility, dependent data marts significantly reduce analytics processing costs.

The data warehouse has a high risk of failure and is challenging to construct since it is massive and interconnected. From the other end, the data mart is simple to build and has a lower chance of failure but may fragment.