Data analysis is one of the most sought-after needs for any organization. Analysis requirements gather speed and momentum, especially if the organization grows over a period spanning multiple units and divisions.
At any point, an entity would like to assess data to understand and make decisions about the entire unit or a sub-division. Data Warehouse and Data Mart are the most preferred tools in such scenarios.
Data Warehouse and Data Mart perform the same task, viz., data analysis; however, they have subtle differences, especially regarding the users served.
Key Takeaways
- Data warehouses store large volumes of structured and unstructured data from various sources; data marts contain subsets of data warehouse information for specific business functions.
- Data warehouses provide a comprehensive view of an organization’s data; data marts offer focused insights for individual departments or teams.
- Data warehouses require significant resources and time to implement and maintain; data marts are smaller, less complex, and quicker to deploy.
Data Warehouse vs Data Mart
The difference between Data Warehouse and Data Mart is that Data Warehouse is a setup for analyzing data at an overall organizational level. At the same time, Data Mart is a subset of a Data Warehouse and is utilized for analyzing data for specific domains/users.
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However, the above is not the only difference. A comparison between both the terms on specific parameters can shed light on subtle aspects:
Comparison Table
| Parameter of Comparison | Data Warehouse | Data Mart |
|---|---|---|
| Meaning | The system used for storing, retrieving, managing, reporting and analyzing large amounts of data | Data Mart is a subtype or subset of Data Warehouse |
| Purpose | For analysis of data | Used for analysis of data but targeted or designed for specific groups or users |
| Implementation perspective | More time due to the complex nature and ability to handle extensive data | Less time due to focusing on specific areas only |
| Subject Area | Not focussed on any specific domains or subjects, it is utilized for the entire business. | It is subject-oriented, For example, analysis of data related to the human resources department |
| Amount of data | Yes | No, because it is specific to some users |
| Macro level or micro level | Used for the entire organization | Focussed only on specific users, hence can be considered as being suitable at a micro level. |
| Which one is more useful? | It depends on specific needs, but overall can be considered more valuable since it provides information about the entire business (including all departments) | It depends on specific needs, but overall can be considered less helpful as it restricts to some domains/user groups. |
What is Data Warehouse?
Data Warehouse is the most preferred system for the management of voluminous data. Data Warehouse can be called a powerful tool for analyzing data.
Data Warehouse is an informational setup to scrutinize, investigate, and analyze cumbersome and vast volumes of data that can be historical or current.
Data Warehouse works on gathering data from numerous sources or applications, processing the same, and finally conducting analysis.
This process helps generate numerous summaries and customized reports for management decision-making. One of the exciting features of Data Warehouse is that the data stored is not erased when new data is added.
Data Warehouse is a boon to an organization so far as data analysis is concerned.
Data Warehouse is principally utilized for reporting, compressing, analyzing, investigating, integrating and summarizing data for making judgments and determinations related to the data.
Data Warehouse embraces sophisticated techniques to enable quick search and accurate analysis.
Data Warehouse has some disadvantages which stop specific organizations from implementing them same. Some of the main demerits include expensive implementation and ongoing maintenance.
Also, the processing time may reduce considerably if the data is too complex and voluminous.
What is Data Mart?
Data Mart is a part(type) of a Data Warehouse.
In simple terms, Data Mart is the access layer of a Data Warehouse environment, which distributes data to specific users. Data Mart can be considered a subset (and an important one) of a Data Warehouse.
Data Mart is subject or target-oriented, meaning it is built to meet the needs of particular organisational groups or departments.
For example, the organisation’s human resources division may be interested in analyzing retention and resignation trends data. In such cases, the Data Mart will help generate the needed results.
Data Mart is simple and easy to manage and comes at less cost. Data Mart utilizes limited amounts of data and processes the same quickly.
Data Mart focuses only on certain specific users/sectors, so it is a boon to assessing data at a micro-level or clear business lines.
Data Mart has some shortcomings. For example, Data Mart can pull data only from limited/few sources, can store only a limited amount of data, and will have certain size limitations.
Also, as the organization grows, there may be a tendency to create too many Data Marts, which can be a complex process. Data Mart cannot be considered an enterprise-wide platform for data analysis solutions.
Main Differences Between Data Warehouse and Data Mart
- Data Warehouse is a system for managing and analyzing vast amounts of data. Data Mart is a type of Data Warehouse.
- Data Warehouse manages data from all departments/businesses as a whole. Data Mart focuses on specific domains/users/groups.
- Data Warehouse implementation and design is a complex process and takes time. Data Mart design and implementation is easy and takes less time.
- Data Warehouse can take large amounts of data, but more processing time will take. Data Mart only takes less data for processing but will process quickly.
- The Data Warehouse size range is large (maybe more than 1TB). Data Mart size is small (only in GB).
- Data Warehouse is more useful for an organization as a whole. Data Mart is more helpful for a single domain/department.
- https://go.gale.com/ps/i.do?id=GALE%7CA18993844&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=00010782&p=AONE&sw=w
- https://dl.acm.org/doi/abs/10.1145/313310.313345
- https://ieeexplore.ieee.org/abstract/document/6108446/
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Sandeep Bhandari holds a Bachelor of Engineering in Computers from Thapar University (2006). He has 20 years of experience in the technology field. He has a keen interest in various technical fields, including database systems, computer networks, and programming. You can read more about him on his bio page.
