Difference Between Data Warehouse and Data Mart
Data analysis is one of the most sought after need for any organization. Analysis requirements gather speed and momentum especially if the organization grows up over a period spanning into multiple units and divisions.
At any point in time, an entity would like to assess data to understand and/or to make decisions related to the entire unit or a sub-division. Data Warehouse and Data Mart are the most preferred tools being utilized in such scenarios. Data Warehouse and Data Mart perform the same task viz. data analysis, however, they have subtle differences especially when it relates to the users being served.
Data Warehouse vs Data Mart
The main difference between Data Warehouse and Data Mart is that Data Warehouse is a setup for analyzing data at an overall organizational level, while Data Mart is a subset of Data Warehouse and is utilized for analyzing data for specific domains/users.
However, the above is not the only difference. A comparison between both the terms on certain parameters can shed light on subtle aspects:
Comparison Table Between Data Warehouse and Data Mart (in Tabular Form)
| Parameter of Comparison | Data Warehouse | Data Mart |
|---|---|---|
| Meaning | System used for storing, retrieving, managing, reporting and analyzing large amounts of any type 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 certain groups or users |
| Implementation perspective | More time due to complex nature and ability to handle large data | Less time due to focus on specific areas only |
| Subject Area | Not focussed on any specific domains or subjects, it is utilized for the entire business as a whole | 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 for certain users, hence can be considered as being suitable at a micro level |
| Which one is more useful? | Depends on specific needs, but overall can be considered as more useful since it provides the information of the entire business (including all departments) | Depends on specific needs, but overall can be considered as 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 as a powerful tool for analyzing data. Data Warehouse is an informational set up to scrutinize, investigate, and analyze cumbersome and huge volumes of data which can be either historical or current.
Data Warehouse works on the philosophy of gathering data from numerous sources or applications, processing the same, and finally conducting analysis. This process helps in generating numerous summaries and customized reports for management decision making. One of the interesting features of Data Warehouse is that the data stored is not erased when new data is being 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 speedy search and accurate analysis.
Data Warehouse has some disadvantages which stop certain organizations from implementing the same. Some of the main demerits include expensive implementation and ongoing maintenance. Also, if the data involved is too complex and voluminous the processing time may reduce considerably.
What is Data Mart?
Data Mart is a part(type) of Data Warehouse. In simple terms, Data Mart is the access layer of a Data Warehouse environment, which is utilized to distribute data to specific users. Data Mart can be considered as a subset (and also an important one) of Data Warehouse.
Data Mart is subject or target-oriented, meaning it is built to meet the needs of particular groups or departments within an organization. For example, the human resources division of the organization may be interested in analyzing data of retention and resignation trends. 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. As Data Mart focuses only on certain specific users/sectors it is a boon to assessing data at a micro-level or specific 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
Conclusion
Data Warehouse and Data Mart are quite similar in their data management abilities. Both offer multiple but distinct sets of benefits and come with certain cons. Data Warehouse and Data Mart serve the same purpose (i.e. data analysis) but they serve different user groups.
Data Warehouse will assist at an organizational level, while Data Mart will support at a departmental level. Therefore, it is important to assess these aspects and also the individual/organizational/divisional needs before deciding to adopt either a Data Warehouse or Data Mart.
A prudent option would be to start with Data Warehouse and later move to Data Mart if there is a specific subject matter need. A thorough practical understanding and advice especially from data management specialists is suggested to reap the full benefits of either the Data Warehouse or Data Mart deployment.
The most important focal point which should always be kept in perspective is whether the system implemented will serve the ultimate purpose of the organization.
References
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