Difference between Data Warehouse and Data Mart

Both Data Warehouse and Data Mart are used for store the data. The main difference between Data warehouse and Data mart is that, Data Warehouse is the type of database which is data-oriented in nature. while, Data Mart is the type of database which is the project-oriented in nature. The other difference between these two the Data warehouse and the Data mart is that, Data warehouse is large in scope where as Data mart is limited in scope. 

Data Warehouse

A Data Warehouse is a centralized repository that allows you to store and analyze large amounts of data collected from different sources. It is specifically designed to handle analytical processing and decision-making. Unlike traditional databases, which are optimized for transactional processing, data warehouses are optimized for read-heavy queries, reporting, and analysis.

Key Characteristics of a Data Warehouse

1. Subject-Oriented: Data is organized around key subjects or business areas like sales, finance, or marketing, rather than around applications or processes.
2. Integrated: Data from multiple sources is integrated, cleaned, and transformed into a consistent format. This makes it easier to analyze, as data from different sources is normalized and stored in a consistent manner.
3. Time-Variant: Data warehouses store historical data that can be used to analyze trends over time. The data is typically stored in time-based periods (e.g., daily, monthly, yearly).
4. Non-Volatile: Once data is entered into a data warehouse, it is not modified or deleted. This makes it different from transactional databases where data may change regularly.

Components of a Data Warehouse

1. Data Sources: The origin of data which may include databases, CRM systems, ERP systems, flat files, external APIs, etc.
2. ETL (Extract, Transform, Load) Process:
   • Extract: Data is extracted from various sources.
   • Transform: The extracted data is cleaned, transformed, and standardized.
   • Load: The transformed data is loaded into the data warehouse for analysis.
3. Data Warehouse Storage: The actual storage layer where data is stored in an optimized structure for querying. This includes both raw data and processed data.
4. Data Marts: Smaller, focused subsets of data warehouses that serve specific departments or business units.
5. OLAP (Online Analytical Processing) Cubes: Used for multidimensional analysis. OLAP cubes allow users to slice and dice data across various dimensions and measures.
6. Business Intelligence (BI) Tools: Tools like Tableau, Power BI, or Looker that connect to the data warehouse and allow users to perform reporting, analysis, and visualization of data.

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Data Mart

A Data Mart is a smaller, specialized subset of a Data Warehouse that is designed to serve the needs of a specific business unit, department, or team. While a data warehouse typically integrates data from multiple sources across the entire organization, a data mart focuses on a specific subject area or business function (such as sales, marketing, finance, or human resources).

Key Characteristics of a Data Mart

1. Subject-Oriented: A data mart focuses on a specific subject area or business unit, such as sales, customer support, or inventory. It only contains relevant data for the department it serves.
2. Smaller in Scope: Compared to a data warehouse, a data mart contains a much smaller volume of data. This is because it focuses on a specific aspect of the business, unlike the comprehensive, organization-wide scope of a data warehouse.
3. Faster to Implement: Because a data mart is smaller in size and scope, it can be built and deployed more quickly than a full-fledged data warehouse. This makes it a good choice for departments that need rapid access to data and insights.
4. Independent or Dependent:
   • Independent Data Mart: It is a standalone system, which means it does not rely on an existing data warehouse. It extracts and processes data from various sources to create a specialized data set for a specific business need.
   • Dependent Data Mart: It is built from an existing data warehouse. The data from the data warehouse is extracted, transformed, and loaded into the data mart, which then focuses on a specific business function.
5. ETL Process: Similar to a data warehouse, data marts rely on the ETL (Extract, Transform, Load) process to collect, transform, and store data, but this process is often simpler and more focused due to the narrower scope.

Types of Data Marts

1. Dependent Data Mart: A dependent data mart is created from an existing data warehouse. The data from the centralized data warehouse is extracted and loaded into the data mart, making it consistent with the rest of the organization’s data.
2. Independent Data Mart: An independent data mart is created directly from operational data sources without relying on a data warehouse. It is typically smaller, and its data integration process may be simpler, but it lacks the data consistency and governance that a central data warehouse provides.
3. Hybrid Data Mart: A hybrid data mart combines data from both a data warehouse and operational systems or external sources. It allows organizations to combine structured and unstructured data in one place for specific analysis.

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Data Mart vs. Data Warehouse

Let's see the difference between Data Warehouse and Data Mart: