So sánh independen va dependen data mart năm 2024

A data mart is the access layer of the data warehouse environment that is used to get data out to the users. The data mart is a subset of the data warehouse that is usually oriented to a specific business line or team. Data marts are small slices of the data warehouse. Whereas data warehouses have an enterprise-wide depth, the information in data marts pertains to a single department. In some deployments, each department or business unit is considered the owner of its data mart including all the hardware, software and data. This enables each department to use, manipulate and develop their data any way they see fit; without altering information inside other data marts or the data warehouse. In other deployments where conformed dimensions are used, this business unit ownership will not hold true for shared dimensions like customer, product, etc.

The reasons why organizations are building data warehouses and data marts are because the information in the database is not organized in a way that makes it easy for organizations to find what they need. Also, complicated queries might take a long time to answer what people want to know since the database systems are designed to process millions of transactions per day. While transactional databases are designed to be updated, data warehouses or marts are read only. Data warehouses are designed to access large groups of related records.

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.

A data mart is basically a condensed and more focused version of a data warehouse that reflects the regulations and process specifications of each business unit within an organization. Each data mart is dedicated to a specific business function or region. This subset of data may span across many or all of an enterprise’s functional subject areas. It is common for multiple data marts to be used in order to serve the needs of each individual business unit (different data marts can be used to obtain specific information for various enterprise departments, such as accounting, marketing, sales, etc.).

The related term spread mart is a derogatory label describing the situation that occurs when one or more business analysts develop a system of linked spreadsheets to perform a business analysis, then grow it to a size and degree of complexity that makes it nearly impossible to maintain.

Reasons for creating a data mart

• Easy access to frequently needed data
• Creates collective view by a group of users
• Improves end-user response time
• Ease of creation
• Lower cost than implementing a full data warehouse
• Potential users are more clearly defined than in a full data warehouse
• Contains only business essential data and is less cluttered.

Stand-alone data mart

A Stand-alone data mart focuses exclusively on one subject area and it is not designed in an enterprise context. For example, manufacturing has their data mart, human resources has their, finance has their and so on. stand-alone data mart gets data from multiple transaction systems in one subject area or department to support specific business needs. stand-alone data mart may use dimensional design or entity-relationship model. Analytic or business intelligence tools query data directly from data mart and present information to user. The picture below is a typical Stand-alone data mart.

Stand-alone data mart takes very short time to build and bring the visible result to specific departments with less cost. However if you look at the whole system landscape where multiple data marts exist, you will see that different ETL tools need to built for different transaction systems in different technologies and the data is duplicate in several data marts. From business perspective, each data mart is built to address a set of specific business needs, what if the needs expand? And what if you want to analyze data across function or department? The inconsistent data, such as definition of product, will make the information comparison between departments impossible.

Dependent data mart

According to Bill Inmon, a dependent data mart is a place where its data comes from a data warehouse. Data in a data warehouse is aggregated, restructured, and summarized when it passes into the dependent data mart. The architecture of a dependent data mart is as follows:

A data mart is a database designed to serve the needs of a particular business or department within an organization. They are built using a subset of the data from an organization's larger data warehouse, which is then organized and optimized for specific analytical needs. By focusing on a specific area of interest, data marts can provide targeted, high-quality data analysis that can help businesses make better decisions.

Data marts are designed to be small and easy to use, with a user-friendly interface that allows non-technical users to quickly access and analyze data. They are often used in conjunction with other data warehousing techniques, such as data lakes or data warehouses, to provide a complete solution for an organization's data needs.

One of the key advantages of a data mart is its ability to provide faster and more efficient access to data than a traditional data warehouse. Because data marts are smaller and more focused, they can be updated and queried faster than a larger data warehouse. This means that businesses can get the information they need more quickly, which can lead to more informed decision-making and ultimately better business outcomes.

Why are Data Marts Important?

By focusing on a specific area of interest, data marts can provide targeted analysis that helps businesses make more informed decisions. This is especially important in today's fast-paced business environment, where decisions need to be made quickly and accurately.

Another reason data marts are important is they can help reduce the complexity of an organization's data architecture. By breaking down data into smaller, more manageable subsets, data marts make it easier to analyze and understand the data. This can lead to better collaboration across different business units and departments, as everyone has access to the same high-quality data.

In addition, data marts can help businesses save money by reducing the need for large, expensive data warehouses. Because data marts are smaller and more targeted, they can be built and maintained at a lower cost than a larger data warehouse. This can be especially beneficial for smaller organizations or those that are just starting to build out their data infrastructure.

Overall, data marts are important because they provide businesses with fast, targeted access to high-quality data that can help them make better decisions. They can also help simplify an organization's data architecture and reduce costs, making them a valuable tool for any business looking to improve its data analytics capabilities.

What are the Benefits of a Data Mart?

Data marts provide several benefits that make them an important component of any data architecture. One of the main benefits of data marts is the ability to improve the efficiency and effectiveness of data-driven decision-making. By providing business users with easy access to the data needed in a format tailored to their specific needs, data marts can help to improve the speed and accuracy of decision-making. Additionally, by focusing on a specific subject area or department, data marts can provide a more targeted view of the data, making it easier for business users to find the information they need. This can help to improve the overall performance of data-driven applications and can help to drive business growth.

Another benefit of data marts is that they can help to improve data quality and consistency. By providing a single source of truth for data definitions and relationships, data marts can help to ensure that data is accurate, consistent, and complete. This can improve the overall quality of data-driven decisions and can help organizations to comply with regulatory requirements. Additionally, data marts can be used to enforce data governance policies, such as ensuring that data is secure and compliant, which can help to prevent data breaches and ensure that sensitive data is only accessed by authorized users.

Data marts also can help organizations to better understand their data. By providing a single source of truth for data definitions and relationships, data marts can make understanding the relationships and dependencies between different data elements easier. This can help organizations better understand their data and make it easier to implement new data-driven applications. Additionally, data marts can be used to improve data integration by providing a consistent view of the data across different applications, even if the underlying data structures change. This can make integrating data from multiple sources easier and reduce the need for custom coding in each application.

Types of Data Marts

There are two main types of data marts: dependent and independent. A dependent data mart is created by extracting data from a larger, enterprise-wide data warehouse. This data is then transformed and loaded into a smaller, more focused data mart that is designed to meet the needs of a specific business unit or department. Larger organizations with complex data architectures often use dependent data marts.

An independent data mart is created by extracting data directly from the source systems that generate it. This data is then transformed and loaded into a separate, standalone data mart that is designed to meet the needs of a specific business unit or department. Smaller organizations often use Independent data marts.

Both types of data marts have their own advantages and disadvantages. Dependent data marts are typically easier to build and maintain because they rely on a centralized data warehouse for their data. However, they can also be more complex and expensive to implement, as they require a larger data warehouse and more complex ETL processes.

Independent data marts, on the other hand, are typically more flexible and cost-effective than dependent data marts. They are designed to meet the needs of a specific business unit or department so they can be built and maintained at a lower cost. However, they can also be more difficult to build and maintain, as they require more direct access to the source systems that generate the data.

In general, the type of data mart that is right for a business depends on a number of factors, including the size of the organization, the complexity of its data architecture, and the specific needs of the business units or departments that will be using the data mart.

Structure of a Data Mart

There are three schema-level and interrelated data architectures for data marts: star, snowflake, and denormalized tables.

Star - The star structure is a common architecture used in the design of a Data Mart. it is a dimensional modeling technique that organizes data into a central fact table and a set of related dimension tables arranged in a star shape. The fact table contains the measurable data, or facts, that are the primary focus of analysis, such as sales revenue or customer orders. The dimension tables provide context to the facts, such as information on customers, products, or time periods. The fact table is connected to each dimension table through a foreign key, which enables users to perform complex queries across multiple dimensions. This structure provides a fast and flexible way to retrieve data for analysis, allowing users to quickly gain insights into business performance.

Snowflake - The snowflake structure is a data modeling technique that builds upon the Star Structure. It is so-called because its diagrammatic representation looks like a snowflake. In this structure, the dimension tables are normalized to reduce redundancy and improve data integrity, resulting in a more complex but more flexible data model. This normalization means that each dimension table is split into smaller tables, with each sub-table containing a subset of attributes or fields from the main dimension table.

The snowflake structure offers the advantages of easier maintenance, more efficient use of storage space, and greater scalability as data volumes increase. However, it can also result in more complex queries that require multiple joins across several tables, leading to longer query times. As such, this structure is often preferred for larger data marts where query performance is not as critical as data consistency and flexibility.

Denormalized tables - Denormalized tables are a design technique used in the creation of a Data Mart that stores redundant data to improve query performance. In contrast to normalized tables, which are structured to eliminate redundancy and improve data integrity, denormalized tables duplicate data to reduce the number of joins required to answer analytical queries.

Denormalization is particularly useful when the Data Mart has a large number of rows, as it can significantly reduce the time required to retrieve data for analysis. By duplicating data, denormalized tables allow analysts to retrieve all the required data from a single table rather than requiring multiple joins across several tables.

Data Marts vs. Other Technologies and Methodologies

Data marts are just one of many technologies and methodologies used for managing and analyzing data. Here are some of the key differences between data marts and other popular approaches:

• Data marts vs. data warehouses - Data warehouses are large, centralized repositories of data that store information from a variety of sources and are used for enterprise-wide reporting and analysis. Data marts, on the other hand, are smaller, more targeted subsets of a data warehouse or directly extracted from source systems, designed to meet the specific needs of a business unit or department. While data warehouses provide a more comprehensive view of an organization's data, data marts can provide focused and relevant data to specific users, resulting in better decision-making.
• Data marts vs. data lakes - Data lakes are large, centralized repositories of raw, unstructured, or semi-structured data from various sources that can be accessed and analyzed by different users and departments for various purposes. Data marts, on the other hand, are designed to meet the specific needs of a business unit or department and are more focused and structured. Data lakes provide greater flexibility for data exploration and analysis, while data marts offer targeted data for specific business needs.
• Data marts vs. ETL - ETL (extract, transform, load) is a process for moving data from multiple sources into a target system, such as a data warehouse or data mart. While ETL is a critical component of building a data mart, it is not the same as a data mart. ETL is a means to an end, while a data mart is a specific outcome designed to meet a particular business need.

Overall, data marts are a useful tool for providing targeted and relevant data to specific business units or departments. While there are other technologies and methodologies available for managing and analyzing data, data marts offer a more focused and efficient approach that can lead to better decision-making and improved business outcomes.