Difference between Data Mart and Data Warehouse

Difference between Data Mart and Data Warehouse

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Rashmi
Rashmi Karan
Manager - Content
Updated on Oct 17, 2022 11:46 IST

The article covers the basic concepts of data mart and data warehouse and the difference between data mart and data warehouse.

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Every business requires well-designed databases to store vast volumes of data. Both data warehouses and data marts are used to store such humongous business data. However, there are some differences between these two that need to be understood before pursuing either of them. This article explains the difference between data mart and data warehouse.

Difference Between Data Mart and Data Warehouse

Criterion

Data Warehouse

Data Mart

Use Helps business managers make strategic decisions Contributes towards making tactical decisions for the business
Goal Provide an integrated environment for the businesses Isolate smaller chunks of data from a huge chunk to help consumers have easy access to the data
Design Complex Simple
Dimensional model It may or may not be used in a dimensional model. However, you can feed dimensional models It is built focused on a dimensional model using a starter scheme
Data management It includes a large area of ​​the corporation, so it takes a long time to process They are easy to use, design, and implement as they can only handle small amounts of data.
Type of data Detailed data is stored Data is short and limited since it caters to a smaller group
Standardization Modern warehouses are mostly denormalized to provide faster data queries and good read performance There is no preference between a normalized or denormalized structure
Data storage Designed to store company-wide decision data Functional wise or single-form data storage
Type of schema used Constellation of facts Star and snowflake
Data value Read-only from the end-users point of view Grouped transactional data fed directly from the Data Warehouse
Data model Top to bottom Bottom-up
Data Source The data comes from many sources The data comes from limited sources like corporate data warehouses, internal operational systems, or external data sources.
Size Size varies from 100 GB to over 1 TB Size is less than 100 GB
Implementation time Spans from months to years Limited to a few months
Ease of construction Difficult to build Easy to build
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What is Data Warehouse?

A data warehouse is the most preferred system for managing and analyzing extensive data. Data warehouses collect data from numerous sources, process it, and analyze them. This process helps generate numerous customized reports and summaries for management decision-making. In the Data warehouse, the stored data is not erased when new data is added.

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Data warehouses have proved to be very helpful in data analytics as they report, compress, analyze, investigate, integrate, and summarize data to make data-related decisions.

A good data warehouse should possess the following characteristics, as defined by the American computer scientist Bil Inmon:

  1. Scalable and flexible: It can grow without problems allowing any organization data and date.
  2. Structured: It contains all the data from all the organization’s systems, and the information is structured at different levels to suit the users’ needs.
  3. Historical: The information stored in the Data Warehouse is also used for trend analysis, allowing comparisons. We are not talking about the present; instead, it brings together a time variable to allow a complete analysis of what is happening and what will happen.
  4. Thematic: It includes the necessary data for the knowledge of the business and is organized by themes to facilitate its access and understanding to the end users.
  5. Non-volatile: The information is not modified or deleted. It is kept for future reference.

It differs from the Data Mart because it does not store specific data, subsets, or unique data sets but processes large data sets.

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What is Data Mart?

A data mart is a type of data warehouse. It is the access layer of a data warehousing environment used to distribute data to specific users. A data mart is topic or goal-oriented, which means it meets the needs of particular groups or departments within an organization. For example, your organization’s human resources division may look to analyze the retention and resignation trend data. In such cases, the data mart will help generate the necessary results.

Features of data mart –

  • Simple and easy to administer
  • Uses limited amounts of data and processes it quickly
  • Enables evaluating data at a micro level

Types of Data Marts

Data markets are classified into three types i.e. dependent, independent, and hybrid. This classification is based on how they have been populated, i.e. from a data store (or) from any other data source.

1) Dependent data mart – The data is obtained from the existing data warehouse in a dependent data mart. This is a top-down approach because part of the restructured data in the data mart is pulled from the centralized data warehouse.

2) Independent data mart – An independent data mart is more suitable for small departments in an organization. Here the data is not fetched from the existing data store. The independent data mart is not dependent on other data marts. Independent data marts are independent systems where data is extracted, transformed, and loaded from external (or) internal data sources. These are easy to design and maintain until they meet the simple business needs of the department.

3) Hybrid data mart – Data is integrated from the DW and other operating systems in a hybrid data mart. Hybrid data marts are flexible with large storage structures. It can also refer to other data from data marts.

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How do Data Mart and Data Warehouse Differ?

As we have seen, they are quite similar terms, but the main difference between Data Mart and Data Warehouse lies in the scope.

  • A data warehouse is an extensive repository of data collected from different organizations or departments. On the other hand, a data mart is a unique subset of a data warehouse. It is designed to meet the needs of a specific group of users.
  • A data mart focuses on a single topic. In contrast, the data in a data warehouse comprises data from all organization departments, continuously updated to eliminate redundant data.
  • Implementing a data warehouse can take many months and even years. The data mart implementation process is restricted to a few months.
  • Data stored in a data warehouse offers more detail than in a data mart.
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Conclusion

Business intelligence and analytics depend on data storage. Without storage, processing or purging the information to conduct a suitable analysis later is impossible. This is what data warehouses and data marts achieve, integrating information from one or more analysis sources with a high response speed.

Data mart and data warehouse are similar terms, but the main difference between a data mart and a data warehouse lies in the scope. A Data warehouse provides an enterprise view and a single centralized storage system. At the same time, a data mart is a subset of a data warehouse that provides a departmental view and decentralized storage. It can be a good idea to start with the data warehouse and then move on to the data mart, considering the organizational business goals.

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About the Author
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Rashmi Karan
Manager - Content

Rashmi is a postgraduate in Biotechnology with a flair for research-oriented work and has an experience of over 13 years in content creation and social media handling. She has a diversified writing portfolio and aim... Read Full Bio