Why do we need Data Mart?
- Data Mart improves user response time because there is less data to process.
- Access to frequently sought data is made simple.
- When opposed to a corporate data warehouse, data marts are easier to implement. However, compared to the expense of constructing a full data warehouse, Data Mart is unquestionably less expensive.
- A data mart is more agile than a data warehouse. A smaller data mart can be constructed more quickly in the event of a model change.
- Partitioning of the data enables highly fine-grained access control privileges.
On a variety of hardware and software platforms, data can be split and stored.
Types of Data Mart
There are three main types of data mart:
- Dependent: Data is directly pulled into dependent data marts from operational, external, or both sources.
- Independent: Without using a central data warehouse, an independent data mart is produced.
- Hybrid: Data from operational systems or data warehouses can be ingested by this kind of data mart.
Dependent Data Mart
A dependent data mart enables data sourcing from a single data warehouse for an organization. One data mart example that offers the advantage of centralization is this one. You must set them up as dependent data marts if you need to create one or more physical data marts.
There are two techniques to build a dependent data mart in a data warehouse. Depending on their needs, users may have access to both the data mart and the data warehouse, or only the data mart. The second strategy is less effective because it results in what is frequently called a “data junkyard.” All of the data in the data junkyard originate from the same source, yet they are primarily discarded and trashed.
Independent Data Mart
Without utilizing a central data warehouse, an independent data mart is produced. Smaller units within a company can benefit greatly from this type of data mart.
The enterprise data warehouse and other data marts are not connected to an independent data mart. The data is entered separately into the Independent Data Mart, and its analysis are likewise carried out independently.
Independent data mart implementation goes against the purpose of creating a data warehouse. First and foremost, you require a consistent, central repository of business data that can be used for analysis by several users with various informational needs and interests.
Hybrid Data Mart
The input for a hybrid data mart comes from sources other than data warehouses. When you need ad-hoc integration, perhaps after a new group or product is added to the organization, this might be useful.
It is the greatest data mart illustration for various database settings and quick implementation for any business. It also requires the least amount of data purification work. Hybrid Data Mart is ideally suited for smaller data-centric applications and also supports massive storage structures.
Steps in Implementing a Datamart
A Data Mart’s implementation is a rewarding yet challenging process. The specific steps to implement a data mart are as follows:
Designing
The first stage of implementing a Data Mart is designing. It includes every activity from putting in a request for a data mart to obtaining needs data. The logical and physical Data Mart design is then created.
- Compiling the technical and commercial requirements, as well as identifying data sources.
- Making the right data subset selections.
- Creating the data mart’s logical and physical structure.
- Date, Business Unit, and Functional Unit, Geography; Any of the aforementioned in combination
What Supplies and Technology Do You Require?
A basic pen and piece of paper would do. Although UML or ER diagramming tools would also include meta data to your logical and physical designs.
Constructing
The second stage of the implementation is now. Both the logical structures and the physical database must be created.
The following duties are involved in this step:
putting into practice the actual database that was planned in the earlier stage. For instance, objects used in database schema, such as tables, indexes, and views, are constructed.
What Supplies and Technology Do You Require?
To build a data mart, you require a relational database management system. A Data Mart must have a number of characteristics that RDBMS have to offer.
• Storage management: To generate, add, and delete data, an RDBMS stores and controls the data.
• Quick data access: Using a SQL query, you can quickly access data that meets certain criteria.
• Data security: The RDBMS system also provides a mechanism to recover from system malfunctions such power outages. In the event that the disk fails, it also enables data restoration from these backups.
• Concurrent access allows many users to access and modify data without interfering with or overwriting changes made by another user. This feature of the data management system is known as multiuser support.
Populating
The data mart is filled with data in the third phase.
The tasks involved in the populating process are as follows:
• Data loading into the data mart,
• source data extraction,
• target data mapping,
• source data cleaning and transformation procedures,
• Data loading into the data mart,
• Creating and preserving metadata
What Supplies and Technology Do You Require?
These population tasks are carried out utilizing an ETL (Extract Transform Load) tool. With the aid of this tool, you can examine the data sources, carry out source-to-target mapping, extract the data, transform it, clean it up, and then reload it into the data mart.
Additionally, as part of the process, the program generates some metadata about the data, including its source, age, type of modifications, and degree of summarization.
Accessing
The fourth stage, accessing, involves using the data by querying it, producing reports and visualizations, and publishing them. Users can submit queries to the database and view the responses.
The following duties must be carried out by the accessing step:
- Create a meta layer that converts the names of items and database structures into business terms. This makes it simpler for non-technical individuals to use the Data mart.
- Establish and keep up database structures.
- Configure APIs and interfaces as necessary
What Supplies and Technology Do You Require?
Using the GUI or the command line, you can access the data mart. The GUI is favored over the command line because it is more user-friendly and can produce graphs quickly.
Managing
The Data Mart Implementation procedure ends with this phase. This level includes management duties like:
• Ongoing control over user access.
• System fine-tuning and optimization to obtain improved performance.
• Managing the addition of new data to the data mart.
• Creating recovery plans and making sure the system is available in the event of a failure.
What Supplies and Technology Do You Require?
The GUI or the command line are both options for managing data marts.
Optimal Techniques for Implementing Data Marts
The following are the recommended practices you should stick to when implementing a data mart:
• A departmentally organized source should be used for a data mart.
• Weeks rather than months or years should be used to measure the length of the Data Mart implementation cycle.
• Because the implementation of the data mart may be challenging, it is crucial to include all stakeholders in the planning and designing phases.
• You should appropriately budget for the costs of Data Mart Hardware/Software, Networking, and Implementation in your plan.
• Even though the Data Mart was built on the same hardware, handling user queries may require a different piece of software. For quick user response, additional processing power and disk storage needs should be assessed.
Benefits and Drawbacks of a Data Mart
Benefits
• Data marts include a portion of organizational-wide data. A particular group of individuals in an organization will find value in this data.
• It is a more affordable alternative to a data warehouse, whose construction can be expensive;
• Since Data Mart was created expressly to meet the demands of its users, it is simple to use. A data mart can thereby speed up corporate operations.
• Data Mart solutions require less time to implement than data warehouse systems. Data Mart implementation is quicker because just a small portion of the data needs to be focused on.
• It has historical data that lets the analyst spot patterns in the data.
Drawbacks
- Without much advantage, businesses frequently develop too many different and unrelated data marts. Maintaining it can become extremely difficult.
Summary
• Describe a data mart: A data mart is a subset of a data warehouse that is targeted at a certain organizational functional area.
• Because the amount of data is reduced via Data Mart, user response time is improved.
• Dependent, independent, and hybrid data marts are the three different types.
• Weeks rather than months or years should be used to measure the length of the Data Mart implementation cycle.
• A data mart is a less expensive alternative to a data warehouse, whose construction might be expensive.
I tried to give information about data mart as much as I could.
Stay healthy :)
Best regards,
Erdem
