Data Marts vs Data Warehouses: The Pros and Cons

Data Marts and Data Warehouses are two different things but they are usually mistaken for the other. In organizations looking to invest in data storage, they usually have a Data Marts vs Data Warehouses pros and cons list so as to decide which one to invest in

Data Marts and Data Warehouses

Data Warehouse Defined

There are many definitions of a data warehouse. One of the most comprehensive definitions is the one given by Techopedia. They define a data warehouse as

A data warehouse is a collection of corporate information and data derived from operational systems and external data sources. A data warehouse is designed to support business decisions by allowing data consolidation, analysis and reporting at different aggregate levels. Data is populated into the DW through the processes of extraction, transformation, and loading. Techopedia

Data Mart Defined

Data Marts and Data Warehouses

Data marts are one of the most important tools for transforming information into insight. According to Techopedia, a data mart can be defined as

A data mart is a subject-oriented archive that stores data and uses the retrieved set of information to assist and support the requirements involved within a particular business function or department. Data marts exist within a single organizational data warehouse repository.
Data marts improve end-user response time by allowing users to have access to the specific type of data they need to view most often by providing the data in a way that supports the collective view of a group of users.

Techopedia

 

Data Mart vs Data Warehouse

Data warehouses are used typically to deal with large data sets while data marts offer more variety. A company usually starts off with data marts if they want to be smart about their data needs and cash flow and then they gradually scale upwards. Hence s data mart is a subject-oriented database that is often a partitioned segment of an enterprise data warehouse.

Data Marts and Data Warehouses

As data marts constitute parts of a data mart, they usually are subject or department based, They are partitioned into segments in an enterprise data warehouse commonly known as EDW. Because a data mart only contains the data applicable to a certain business area, it is a cost-effective way to gain actionable insights quickly.

Keep in mind that bot data marts and data warehouses are highly structured repositories and they contain important data. Even thought scope might be different – data warehouses are more central and contain data for the entire organization while data marts contain partitioned and sometimes isolated data, they both have proper security and access clearance procedures in order to protect the integrity of the data. Hence, one of the primary purposes of a data mart is to partition or separate a smaller set of data from a whole to provide easier data access for the end consumers. This way different people can have access to some data but not all of the data. This helps maintain the integrity of the entire data set stored in the data warehouse. A data mart is a  sub-set data storage system can be created from an existing data warehouse. Also, separate business units or departments can create their own data marts from scratch and these separate data marts can be merged together to create a data warehouse.


Types of Data Marts

There are three main types of data marts and they are categorized based on how they relate to the data warehouse.

Data Marts and Data Warehouses

Dependent Data Marts

A dependent data mart is created from an existing data warehouse and has a top-down approach to how it is created and operated. After all existing data is stored in the data warehouse, it is then carefully divided into parts and sub-sets which constitute the data marts. These data clusters are aggregated and can be quired using different methods. The main methods are logical view, physical subset, and granular data. Each has its role to play and each has pros and cons that every organization should consider before making data mart decisions. Dependent data marts can take on the job and burden of dat processing and this improved the efficiency of the data warehouse.


Independent Data Marts

Independent data marts as the name implies, are stand-alone systems that are created without the use of a data warehouse. They are not created by dividing or separating a data warehouse, instead, they are created as stand-alone entities that focus on one area or department of a business organization. The data in independent data marts can be extracted from both internal and external sources. Independent data marts are great for achieving short-term goals or projects and as the data needs expand they might be developed into a data warehouse or merged with other data marts. When dependent data marts are placed in a separate processing facility, they significantly reduce analytics processing costs as well.

 

Hybrid Data Marts

Speaking of merging data marts, a hybrid data mart is formed by combining existing data warehouses with other operational systems. A hybrid data mart can be a time-saving solution to organizations that do not have the luxury to build a data warehouse just yet. They are also relatively cheaper to set-up and operate and can improve the performance of a business organization. Dependent and hybrid data marts can improve the performance of a data warehouse by taking on the burden of processing, to meet the needs of the data analyst. When dependent data marts are placed in a separate processing facility, they significantly reduce analytics processing costs and make the job of the data analyst easier. This frees of time and manpower to focus on other more analytical aspects of data management that could be very beneficial to the organization.