Data Warehouse

A data warehouse is a type of data storage and management system that consolidates data from multiple sources in a structured form. A single data warehouse can store data from multiple databases. This central repository of all business data creates a single source of truth across the organization. Over a period of time, these historically accumulated databases become an organization’s irreplaceable asset.

Data warehouses are designed to support business intelligence and enable data scientists and analysts to leverage data for valuable insights. These data-driven insights help in improving the quality of decision-making.

Elements of Data Warehouse

A standard data warehouse consists of:

• A relational database for data storage and management
• An ETL solution for preparing data for analytics
• Capabilities for statistical analysis, data mining, and reporting
• Data visualization tools for presentation and communication of insights to business users
• Other sophisticated analytical applications with data science and AI algorithms for detailed analysis of data

Data Warehouse vs Database vs Data Lake

Data warehouse is not the same as database or data lake. Although the three terms are used interchangeably many times, there is a fine line of difference between them.

Database is created when data is stored for transactional purposes. Database provides users with read and write access. Analytics on data is not possible in a database. They are created to record and retrieve information. However, database form a part of data warehouses. A data warehouse contains aggregated transactional data, processed, and stored for the purpose of analytics. Thus, data warehouse enables analytics on data from multiple databases simultaneously.

Data warehouse and data lake are very similar to each other. Both are built to enable big data analytics. However, the difference between the two is that data warehouse stores data in an organized way, in the form of tables and schemas, to make it readily available for analysis and reporting. Data lake on the contrary stores data in its native form (raw, unstructured, and structures) from all sources, and structuring is done only when needed at the time of analysis.