Data Mart is a data warehousing concept that focuses on a single organization’s single functional area. It ideally consists of a data subset that is stored in the data warehouse. Data Mart can also be called a condensed version of the parent data warehouse, which is ideal to be used by a specific department or a set of users. We can see sales, marketing, or finance as the user set of Data Mart, in which the operations are controlled within a single department of the organization. Data Mart draws data from a limited source compared to that of a data warehouse, which takes data from variant sources.
Data Mart is also smaller in size and so more flexible and less complicated than data warehouses.
Needs of Data Martin enterprise database administration
- Data Mart will help expedite the response time with a reduction in data volume compared to the whole warehouse.
- It can offer easy and quick access to data, which is frequently requested.
- Data Mart is much simpler to set up compared to large data warehouses. Similarly, the cost of implementing a Data Mart is much smaller than data warehouse implementation.
- Data Mart is much agile compared to warehouses, and in case of any change in the model, Data Mart can be quickly built due to its small size.
- Data Mart is defined by an SME (Subject Matter Expert). However, a data warehouse needs to be determined by many interdisciplinary SMEs with expertise in various domains. So, Data Mart is more lenient to changes compared to bigger warehouses.
- Data partitioning is possible, which enables granular access to the control privileges.
- Data can be easily segmented and more effectively stored on different software and hardware platforms.
Types of data marts
There are different types of Data Marts being used nowadays as:
Dependent Data Mart: These are ideally created by drawing the data directly from an operational and external source of both.
Independent Data Mart: These types of Data Marts are created without using any central data warehouse.
Hybrid Data Mart: This is the mix of the above models of Data Mart as the data can be taken from operational systems and warehouses.
Among the above, dependent Data Marts will allow better sourcing of the organizational data from a data warehouse and function more effectively. This is one typical data mart use case that offers all benefits of data centralization. If there is a need to develop one or multiple Data Marts, you can configure them as many dependent Data Marts.To decide what type of Data Mart you have to design based on your data stores, you may consult the expert advisors at RemoteDBA.com.
You can create Dependent Data Martin in two ways. It can be either by how users can access both Data Mart and data warehouse separately, based on the need, or that the access can be limited to the Data Mart only. The latter approach is suboptimal as it may produce a data junkyard sometimes. In the junkyard, data maybe with one common source but can be further scrapped and junked.
Independent Data Marts
Independent Data Marts are usually created without using any centralized data warehouse. These Data Marts are ideal for smaller groups within the organization. Independent data marts are not concerning the enterprise data warehouses or other Independent Data Marts. Data is put separately in each Data Mart, and the access and analyses to are autonomously performed in Independent Data Marts. Implementing Independent Data Marts antithetical to the idea of building a data warehouse. For this, first, you need a consistent and centralized enterprise data store, which can be analyzed by multiple users with varying interests for accessing a wide variety of information.
Hybrid Data Marts
As the name suggests, Hybrid Data Marts combine inputs from various sources apart from their parent data warehouse. This will ideal if you want to do ad-hoc integration like another new product or group is added within the organization. This is also an ideal choice of Data Mart suitable for multiple databases for faster implementation turnaround. It also requires only minimal data cleansing efforts. Hybrid Data Marts may also support larger data storage structures and be best suited for much smaller and more flexible data-based applications.
Implementing Data Marts
Implementing an appropriate Data Mart is a complex process but highly rewarding once done well. Let us further discuss the steps in implementing a Data Mart.
Designing the Data Mart
This is the most fundamental phase of implanting a Data Mart. It can cover all the tasks from the request for a Data Mart to gather information and design a logical and physical Data Mart plan. Various steps included in Data Mart designing are:
- Gather technical and enterprise requirements.
- Identify various data sources.
- Select the appropriate data subsets.
- Design the logical structure of the Data Mart.
- Design the physical structure of Data Mart.
In standard use cases, data may be partitioned based on the following criteria like date, the functional unit inside the business, geography, and/or combinations. Data may be portioned at the DBMS or application level.
Construction of Data Mart
At this next phase of Data Mart implementation, you have to create a physical database’s logical structures. You need to implement the physical database as prepared in the design phase. The database schema objects like indexes, tables, views are created. You may use an RDBMS system for the creation of a data mart. Relational database management systems feature many things as required for a good Data Mart for data storage management, fast data access, data protection and security, multi-user support, etc.
Once the data mart is successfully constructed, you need to do the data population, data mapping, accessing, and managing the same. Data Marts demand only less time for implementation compared to other data systems. You may first try to understand your data subsets based on your various departmental needs and then start to design your Data Mart, making data management much quicker and easier than dealing with huge data warehouses.