A data mart is an access layer of a data warehouse focused on a specific line of business, function, or department. It is used for retrieving client-facing data. A data mart contains a subset of the data that is stored in a data warehouse.
Data warehouses are enterprise-wide data storage facilities. Getting data mart access layers oriented to a single specific organizational area enables users in departments/functional areas to swiftly, easily, and effortlessly access their data.
A data mart can also be described as a compact data warehouse. Hence, in an organization, functional areas such as finance, human resources, marketing, etc., can all be given their own data marts that are parts of a data warehouse administered at the head office level.
Data Mart vs. Data Warehouse
Data marts contain a subset of the information in a data warehouse. For this reason, data marts offer better querying speed for analysts as they naturally contain fewer data.
The size of a data mart will differ within each functional area as some data marts require sizable storage. Data marts are read-only formats with controlled updates performed only by authorized personnel.
Types of Data Marts
There are three major types of data marts. The difference between them is determined by their relationship to the data warehouse and the data sources used to create them. They include the dependent, independent, and hybrid data marts.
1. Dependent Data Marts
Dependent data marts are created using a subset of data from an existing data warehouse. It is a top-down structure in which all the enterprise data is stored in a central location.
Dependent data marts are dependent on the information extracted from enterprise data warehouses. The lowest level of data in an enterprise data warehouse is called granular data and acts as the sole reference point of all created dependent data marts.
The extraction process involves restructuring enterprise warehouse data and loading it onto the requesting data mart for querying. The process uses a logical view, which is a virtual table not physically separated from the warehouse or a physical subset. The extracted data is a physically distinct database from the enterprise data warehouse.
2. Independent Data Marts
Independent data marts are standalone systems that are not affiliated with data warehouses. Data is extracted from internal or external sources and loaded to the independent data mart repository to be used for analysis when needed. Such data marts are narrowly focused on a particular business function or subject area.
Independent data marts are suitable for small businesses as they are easy to set up. However, as the business grows, it can become complex to manage through the assignment of a corresponding ETL tool and logic for each system.
3. Hybrid Data Marts
Hybrid data marts combine sources of primary data from an existing data warehouse and other external data sources. The hybrid data mart cohesive style benefits from the speed and end-user-focused top-down approach and the enterprise-level integration of independent data mart bottom-up approach.
Data Mart Structures
A data mart structure is a subject-oriented relational database that stores data in tables, i.e., rows and columns that are easier to access, organize and comprehend. Data fields can refer to one or multiple objects.
Data marts are structured in a multidimensional schema that works as a blueprint for data analysis by users of the database. The three main structures or schema for data marts are star, snowflake, and vault.
The star schema is a blueprint that resembles a star shape and consists of fact tables that reference dimension tables in a relational database. The fact table is placed at the center of the star and relates a metric set that relates to a specific process.
The star schema requires fewer joints when writing queries as there is no dependency between dimension tables. The ETL request process makes it vastly efficient for accessing and navigating large data sets. The said benefits make star schemas widely used in most information technology systems.
A snowflake schema extends the star schema blueprint with additional dimension tables that are normalized to protect data integrity and minimize data redundancy. The snowflake schema’s main benefit is that it requires less storage space for dimension tables.
However, a snowflake structure is difficult to maintain due to multiple tables that need to be populated and synchronized. It also adversely impacts performance as a result of the need for additional dimension tables.
The vault schema enables users to design agile enterprise data warehouses. It is a fairly modern database modeling technique. The vault schema is a layered structure that focuses on agility and scalability.
Rationale for Data Mart Creation
Provides easy and quick access to regularly requested data
Improves end-user response time
Easier and less costly to create a data mart as compared to a data warehouse
More flexible to changes due to its smaller size compared to a data warehouse
Contains the most relevant and essential data
Easy to navigate and query
Stores segmented data stored on different software platforms and allows granular access control privileges
Uses of Data Marts
Efficient access to information: It is more efficient to access specific data in a data mart that is relevant to real-time needs. Data marts hold a subset of data warehouse information which makes it quick and easy to retrieve information.
Cost-effective alternative to data warehouse: It is more cost-effective to create and design an independent data mart than creating a data warehouse, especially when it comes to small businesses or projects with smaller data sets. Setting up a data mart incurs a small fraction of costs compared to data warehouse setup costs.
Increased processing efficiency: Using dependent and hybrid data marts reduces the burden for processing by data warehouses, thereby improving performance. A separate processing facility for the two data marts will help reduce analytics processing costs.
Efficient data maintenance: It is easier to maintain a data mart because it accesses leaner and less cluttered information. Also, a data mart requires less storage, which is easier to maintain. Different business units are able to maintain and own their own data.
Faster implementation: A data mart requires a small subset of data to set up instead of significant setup costs required for a data warehouse, which contains a large collection of external and internal data.
Business intelligence: Data marts enable quicker insights into strategic information contained in a data warehouse. Business intelligence benefits the organization through accelerated information access and potentially higher productivity.
Analytics: It is easier to track key performance indicators through a data mart.
Data Mart and Cloud Computing
The increase of big data in business is putting the future of data storage in cloud computing. As more data warehouses move to the cloud, so too will data marts. Cloud computing gives them access to quickly scalable.
Cloud-based platforms can facilitate the consolidation of all data in one repository where all data marts will be contained. They provide efficient storage, real-time easy access, and efficient data analytics, as well as cost savings. Modern technologies can split data storage from computing, which allows for maximum scalability for data querying.
Cloud-based storage platforms allow the storage of big data sets and also enable easy and efficient information access and analysis. Additionally, they enable the seamless creation and sharing of data. Cloud-based storage platforms can grow sustainably as data sets become even bigger. Transient and long-term data structures can be created to permit short-term and long-term analysis.
Some of the merits of cloud-based data marts are below:
Efficiency in storage and accessibility
A single repository can contain all data marts
Real-time access to information
Cloud-based architecture is flexible and contains cloud-native applications
On-demand resource consumption
Consolidation of resources lowers costs
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