What is a Data Warehouse?
Imagine having a business and accessing tons of available information from different data sources. Tiring, right? That’s where the data warehousing comes into picture. It basically aggregates data from many different sources to single destination. It helps businesses streamline their workflow and stay on top of their game.
Think of it as a giant organized office achieve specially designed to find out trends and patterns over time. Unlike operational databases focused on day-to-day transactions, data warehouses are subject-oriented, meaning they cater to specific business areas like sales, marketing, or finance.
Why data warehouse?
One of the major reasons to use a data warehouse is as we discussed earlier- It acts as a single source of truth. Here are some of the other reasons why organizations use data warehouses:
- Centralized Data Storage
- Enhanced data quality
- Historical Analysis
- Query Performance
- Scalability
- Business Intelligence
- Regulatory Compliance
- Improved Decision-Making
How is it different than Data Lakes?
Data warehouses, unlike data lakes, store structured data. Data that is cleaned, organized, and stored in specific formats. Data lakes always store the data into raw format.
General Guidelines on implementing a data warehouse
Building a data warehouse includes a few steps:
- Defining Business Requirements
- Data modeling
- ETL(Extract, Transform, Loading) or ELT(Extract, Loading, Transform)
- Indexing and Optimization
- Metadata Management
- Security and Access Control
- Testing and Validation
- Iterative Improvement
The Analytics Clan 