What is better: Data Warehouse or Data Mart?
Every organization knows the importance of data. Strategic advancements can only be made if concentrated efforts are made to store and utilize data correctly and timely. It is that one thing that supports all present and future decisions. In a bid to decide on the best form of storing information, many start-ups and even well-established companies become confused about the form they should choose. Often, the dilemma is choosing between a data warehouse or a data mart.
Apart from the fact that both a data warehouse and a data mart are used to store information, the two are extremely distinct. Hopefully, this post will clarify all your doubts and make for an enlightening read.
Before we begin, let us briefly have a look at what constitutes a data warehouse and a data mart.
Data Warehouse
In simplest terms, a data warehouse is a massive collection of business-related information. The data stored in a data warehouse can be sourced from several places and used at convenience according to the needs of the business. A data warehouse serves to support the very critical function of query analysis.
Data Mart
A data mart is a subset of a data warehouse. The information stored in it can relate to a particular line of business, a product, a particular department, or a specific subject. In short, it is only a small part of the complete business data.
Data Warehouse Vs. Data Mart
Area Of Focus
- Data Warehouse – A data warehouse is data-oriented in nature. It means you will find all the business information here in one place. It is a centralized or integrated repository of data.
- Data Mart – A data mart is project-oriented in nature. It means you will only find a specific type of information in this database. It is a decentralized repository of data.
Scope
- Data Warehouse – The scope of a data warehouse is immense as it contains the data collected from all the departments. One can use the information stored here for a variety of different purposes. If you want to run a query, the data is all here. Use it in the most productive manner.
- Data Mart – In comparison, the scope of a data mart is very limited. It is because the information stored here pertains to just one aspect, department, or product. Therefore, much flexibility is not possible. You can only run queries pertaining to a specific thing.
Usage
- Data Warehouse – It is helpful in making strategic decisions.
- Data Mart – It is useful in tactical decision-making for the organization or the business.
Size
- Data Warehouse – A data warehouse is generally more than 100 GB in size. It may even run into terabytes if your business is big.
- Data Mart – A data mart is less than 100 GB in size. You can well imagine how small that is in comparison to a data warehouse.
Ease Of Designing
- Data Warehouse – Designing a data warehouse is not easy. It is a complicated process due to the sheer number of things a business needs to capture and the numerous sources from which this data will come.
- Data Mart – A data mart is easy to design because the information needed to build it is already there in the data warehouse. It is only a matter of pulling and putting it together in the most relevant and productive form.
Implementation Time
- Data Warehouse – Setting up a data warehouse on-premises can take anywhere from months to years. Implementing it on the cloud is far quicker and can be easily achieved in a few days to weeks at best.
- Data Mart – Implementation of a data mart on-premise will take a few weeks to months, and on the cloud, the same can be achieved within days to weeks.
Cost Consideration
- Data Warehouse – The expense of implementing a data warehouse on-premise will be more than $100 K.
- Data Mart – Setting up a data mart will cost in the vicinity of $10 K, which is way less than the expense of a data warehouse.
Life Span
- Data Warehouse – The data warehouse has a long life. It is because historical data can be used for critical insights even during the later years.
- Data Mart – A data mart has a comparatively short life because the data pertains to a specific thing, and once its purpose is served, one may no longer need it.
Type Of Data
- Data Warehouse – In a data warehouse, the data is stored in great detail in whatever form it is available (structured, unstructured, or semi-structured).
- Data Mart – A data mart contains summarized versions of information, and hence, what you can hope to find here is only well-structured data that has already been through some processing. Since it is built for specific user groups, it contains only limited data.
Schema Used
- Data Warehouse – The schema used in a data warehouse is the Fact Constellation schema.
- Data Mart – The schema used in a data mart can be Snowflake schema or Star schema.
Model Type
- Data Warehouse – A data warehouse will always have a top-down model.
- Data Mart – On the other hand, a data mart will always have a bottom-up model.
The Conclusion
A data warehouse is a one-stop destination for all enterprise data. Its drawbacks include the heavy cost of implementation, the difficulty of designing one, and the time it takes to build. But these are the drawbacks only if you want to build it on-premise. Thankfully, that is no longer a necessity. Businesses can now easily opt for cloud-based data warehouses and cut their costs exponentially.
A data mart may be easy to design, cost only a fraction, and take only a few days to build, yet it will not serve the purpose of the complete organization. It is subject-oriented, and therefore, you will be forced to build a separate one for each subject, department, and line of business. As it is, you can build a data mart anytime from a data warehouse. So, what is more important is to have a data warehouse that is most up-to-date, scalable, and ready to meet the subject specific as well as organizational query and analysis demands of the business.
Disclaimer: The information on this website and blog is for general informational purposes only and is not professional advice. We make no guarantees of accuracy or completeness. We disclaim all liability for errors, omissions, or reliance on this content. Always consult a qualified professional for specific guidance.
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