A data warehouse is a central repository of facts and information that sources and maintains historical and cumulative data from various internal and external systems. Data warehousing comprises of a mix of technologies that allow users to transform scattered, raw data into actionable intelligence, get a single version of the truth, and improve the overall decision-making process.
The main purpose of a data warehouse is to store huge amounts of data for query and analyses. It facilitates analytical and reporting processes that help users make data-backed routine and strategic business decisions.
The Data Warehouse Architecture
A data warehouse comprises of several components. Data from different operational systems is extracted, transformed, and loaded into the staging area, where data undergoes standardization and profiling. In the integration layer, the ‘cleansed’ data is integrated into a unified form and sent to the data warehouse for storage. Through analytical processing, the data is segmented into subsets and transferred into various data marts based on the required information by specific users for reporting purposes.
Difference Between a Database and a Data Warehouse
A data warehouse is often confused with a database. Essentially a transactional system, a database oversees and updates data in real time, providing users with the most recent version of the data. In contrast, a data warehouse comprises of aggregated structured data extracted through the extract, transform, and load (ETL) process. Unlike a database, data doesn’t originate in a data warehouse; instead, it is sourced from different information and management systems.
A data warehouse environment comprises of a relational database, an ETL solution, an OLAP engine, and a data analysis tool, along with other applications used to collect data and deliver useful insights to business users.
The Importance of a Data Warehouse
In organizations, data is dispersed into a variety of internal and external systems that contain information in various formats, orientations, and file types. In addition, some of these systems carry similar data content, causing duplication that may cause inconsistencies in analytics and reporting processes.
Decision makers require accurate, single version of information without any redundancies to gauge business factors, market conditions, consumer behaviors, and other aspects. From a complete list of enterprise customers to the highest number of sales in a month with details of transactions, users can acquire information at all levels.
Data warehousing is geared towards producing complete, well-defined reports that ensure a high level of accuracy. This allows business leaders and analysts to drill down into specific details that they may not be to understand through bits and pieces of information stored in individual databases. In addition, it helps perform data mining on current and historical trends for identifying important patterns and extracting intelligible insights for predictive analytics.
Data warehouses increase the performance of operational systems and databases, which are designed to handle relatively smaller amounts of data, by taking the load of storage and accessibility off them. Generally, organizations have millions of records accumulated over time, which can be of terabytes or petabytes in size. With the help of a data warehouse, all the data can be easily stored without burdening any individual systems.
Finally, having a data warehouse is essential to tap into the power of business intelligence (BI). The data, which has been profiled, cleansed, and checked for quality assurance, is fed into BI tools to provide analytics and visual representation of facts and figures for making well-grounded decisions. In addition, users can compare data in specific timeframes to perform trend analysis of the consumer market and industry.