Data Warehousing is the framework responsible for integrating various data sources and models to achieve business goals.
As a vital constituent of BI, it’s a blend of technologies that aids the strategic use of data – structured, semi-structured and unstructured.
The main emphasis of a data warehouse is to offer a correlation between data from existing systems.
Data analysis tools like Pentaho, Tableau, Jaspersoft, Power BI, SQL clients, and spreadsheets, access the data within the warehouse.
The idea of data warehousing goes back to late 1980s when IBM scholars Barry Devlin and Paul Murphy established the “business data warehouse” concept.
- Data sources from working systems, such as Excel, ERP, CRM etc.
- Data staging area where data is cleaned and ordered
- Presentation area where data is warehoused
Functions of Data Warehouse Tools
- Data Extraction – Collecting data from disparate sources
- Data Cleaning – Extracting errors lying in data
- Data Transformation – Converting legacy data into required format
- Data Sorting, Consolidation, Summarization
The New Age Enterprise Data Warehousing (EDW)
- Data lakes
- Data split across organizations
- IoT streaming data
- Self-service / self-adapting analytics
Factors driving the future of EDW are agility in cross-functional teams, cloud-based computing and next generation of data.