Why is a Data Mart important for businesses?
Data Marts provide essential benefits for businesses as they provide a simplified and quick approach to accessing specific data, allowing business users to make well-informed decisions based on up-to-date information. By storing structured data from different sources in a separate Data Mart, businesses can streamline data access and minimize the complexity of querying data across its entire warehouse.
Data Marts enable businesses to focus on specific business functions or departments and generate tailored reports and analytics to facilitate business intelligence and enhance decision-making processes. Furthermore, Double.Cloud can help extract more insights from Data Marts by leveraging advanced analytics tools and machine learning algorithms to discover hidden patterns and relationships in the data. By utilizing the Double.Cloud platform, businesses can fully utilize their Data Marts and gain valuable insights that can help drive business growth and success.
How does a Data Mart work?
Data Marts are designed to provide quick and simple access to particular categories of data, such as sales, financial, or marketing data, allowing business users to evaluate and produce reports more quickly. The typical purpose of this data storage system is to hold structured data in a relational database, with fact tables containing transactional or historical data and dimension tables giving contextual information like date or customer.
By having independent Data Marts, businesses can avoid the complexity of querying data across an entire data warehouse and instead focus on specific areas of interest. Additionally, the Double.Cloud platform offers sped-up data access to Data Marts, allowing business users to derive insights from data using simple querying and visualization capabilities.
Types of Data Marts
There are three types of Data Marts which all possess distinctive characteristics that make them suitable for specific purposes.
Dependent Data Mart
A dependent Data Mart is one that draws its data sources from an existing data warehouse. It is designed to meet the demands of a particular department or business unit inside an organization by offering a subset of data that is tailored to the unit’s needs. Dependent Data Marts are generated utilizing the data that has already been extracted, transformed, and loaded (ETL) into the organization’s central data warehouse.
This is opposed to independent Data Marts, which can be developed without relying on an existing warehouse for enterprise data. Dependent Data Marts can be built more quickly and with less complexity, while still ensuring data integrity and consistency throughout the entire organization by utilizing the data stored in the warehouse. This is accomplished by using pre-built, warehouse-stored dimension tables that can be quickly accessed and used by the dependent Data Mart to generate unique reports and analyses.
Independent Data Mart
This is called independent specifically because it is made to meet the requirements of a particular department or business unit within an organization. Only the information relevant to that unit or department is contained in it, which was constructed separately from the main warehouse. Agile enterprise data warehouses benefit greatly from independent Data Marts because they can be designed and implemented more quickly than centralized ones.
The independent Data Mart, which can store transactional and historical data, is regularly updated. Users can access the data easily with simplified data access and querying data. The business unit can have more control over their data thanks to this kind of Data Mart, which also enables more focused data analysis to support business intelligence.
Hybrid Data Mart
A hybrid Data Mart is a combination of both independent and dependent Data Marts, allowing organizations to maintain a balance between flexibility and control. It is typically used when business units have specific requirements that cannot be met by the centralized data warehouse, but they still need to adhere to data governance policies. The hybrid data system takes advantage of the strengths of both independent and dependent Data Marts.
It provides agility and speed in data access like independent Data Marts while ensuring data consistency, data redundancy, and data integrity, like dependent Data Marts. This type of system allows organizations to efficiently manage and use their data by combining the benefits of both independent and dependent Data Marts, allowing for simplified data access, efficient querying of data, and support for business intelligence and data mining.
Characteristics of Data Mart
Data Marts possess different characteristics that show its type of design and how it works. The following are four characteristics that define Data Marts.
One of Data Mart’s defining features is its subject-orientedness. As opposed to a traditional data warehouses, is created to specifically address the requirements of a given business unit or function. Instead of being organized around the needs of the enterprise as a whole, the data is usually organized around a particular subject area, such as sales or inventory. Business users can now more quickly and easily access the data they require without having to wade through extraneous information.
The information in a Data Mart is typically obtained from a central warehouse or other data sources, but it is arranged and displayed in a manner that is specific to the particular business unit or function. In addition to lowering the risk of data redundancy and enhancing data governance, this helps to ensure that the data is accurate, consistent, and up-to-date.
A Data Mart’s ability to integrate data from various sources to produce a single, comprehensive view of information is referred to as its integrated characteristic. As a result, the information kept it is not only subject-oriented but also combined from a variety of sources to give users a thorough understanding of a particular business function or process. Data from both internal and external sources, such as data lakes, relational databases, and business intelligence tools, are incorporated into the process of integration.
Data Marts give business users a centralized location to access data that is consistent, accurate, and up-to-date by combining data from various sources. By integrating these systems, data provided is guaranteed to be accurate and usable for data mining and querying, enabling businesses to base decisions on specific data trends and insights.
The 'Time-variant' characteristic refers to how data changes over time. In a Data Mart, data is stored in a way that allows for analysis of trends and patterns over time. This means that the data stored in a marts is time-stamped, enabling business users to query and analyze historical data to identify trends and patterns in the data. By capturing changes in data over time, Data Marts provide insights into how business data has evolved, which can help organizations make data-driven decisions.
Additionally, the time-variant nature of Data Marts allows businesses to track changes in specific data over time, which is particularly useful for tracking trends in sales, customer behavior, and other business functions.
Data Marts are non-volatile, meaning that once data is stored, it cannot be altered or updated. This characteristic ensures data integrity and consistency, which is crucial for business intelligence and decision-making. Unlike operational databases that are subject to frequent changes, Data Marts store historical data that is relevant to specific business functions or departments. This allows for simplified data access and querying, as well as faster response times when retrieving information.
Data Marts can be independent or part of an existing enterprise warehouse, depending on the organization’s data management system and business requirements. While Data Marts are focused on a specific subset of data, they can still access and incorporate external data sources to support data mining and provide a comprehensive view of business trends.
Data Mart Architecture
Understanding Data Mart’s Architecture will bring you closer to grasping the orientation and potential it can offer.
Here are known Data Mart architectural approaches.