A data warehouse is a central repository of data extracted from disparate sources such as transaction systems, operational databases, and external data stores. As against a database that stores current data to run an application, a data warehouse integrates historical data with real-time feeds to enable business reporting and data analysis.
Importance of a data warehouse
Data warehousing is one of the core processes in business intelligence (BI) reporting. A data warehouse allows enterprises to integrate and manage heterogenous business data sources from a centralised location. Warehoused data is easily available for on-demand use, review, and analysis, which empowers businesses to make informed decisions.
How data warehousing works?
Data warehousing works by integrating disparate datasets and transforming them into multidimensional schemas for easy analysis and interpretation. A data warehouse typically has three layers:
- A bottom layer that houses a relational database
- A middle layer with online analytical processing (OLAP) servers to analyse the stored data
- A top layer that comprises the front-end client interface
The bottom layer extracts raw data from multiple touchpoints such as point-of-sale systems, websites, and mailing lists, and cleans it and organises it into a relational database system. The middle layer deploys semantics to map complex data and convert it into natural language. The top layer uses data mining, analytics, and artificial intelligence (AI) to make the data user-friendly and fit for interpretation.
Components of a data warehouse
Data warehousing is a complex process involving multiple tools and processes. Most of these tools belong to one of four key components:
- Central database: Traditional data warehouses were housed on premise or in the cloud. But due to the need for high-speed performance, in-memory databases are now becoming popular.
- ETL tools: ETL (extract, transform, load) tools extract data from source systems, combine and process it, and then load the enriched data into the target database.
- Metadata: Metadata describes the source data, adds context, and makes it intelligible.
- Access tools: These include tools for query and reporting, application development, analytical processing, and data mining. Data access tools allow authorised users to interact with the data through a user-friendly interface.
Benefits of a data warehouse
Data warehouses provide many benefits to business owners. The most common benefits are:
- Providing a centralised repository to store large amounts of historical data
- Improving data quality
- Improving data security with built-in safety features
- Integrating and processing disparate datasets at high speed to provide precise information on business operations
- Using AI and machine learning (ML) to provide actionable insights and improve decision making
- Enhancing BI processes and overall efficiency