Introduction to Data Warehouse

What is a Data Warehouse?

• Data Warehouse: A centralized storage area/repository where data from multiple sources is stored.
• Purpose: Store large amounts of data to process and derive useful business insights.

Real-Life Analogies

• Potato Warehouse: Farmers bring potatoes from various locations and store them in a centralized cold storage.
• Flipkart Warehouse: Items from multiple sources are brought and stored in a centralized location.

Key Processes in Data Warehousing

• ETL (Extract, Transform, Load): Process of extracting data from multiple sources, integrating, and loading it into the warehouse.
  • Extract: Retrieve data from multiple sources.
  • Transform: Integrate and process the data.
  • Load: Store the processed data.

Tools for Data Integration

• Oracle Data Integrator: Integrates data from multiple sources.
• Microsoft SQL Server Integration Services (SSIS): Another tool for data integration.

Data Processing and Cleaning

• Post extraction, data is processed and cleaned to remove irrelevant or meaningless data.
• Similar to cleaning potatoes after they are brought to the warehouse.

Data Modeling in Warehouse

• Structured Data: Uses RDBMS tools, star schema, and snowflake schema.
• Microsoft Visual Software: For data structuring and modeling.

Utilizing Stored Data

• Data Usage: Analyzing and deriving meaningful insights to grow business and make informed decisions.
• Tools for Analysis:
  • Python
  • R Programming
  • Visualization Tools: Tableau, Microsoft Power BI

Physical vs. Cloud Storage

• Large Companies: Build physical storage facilities with necessary infrastructure (routers, switches, hard disks, controlled temperature and humidity).
• Small Companies: Often use cloud services like Google Big Query due to lower costs and infrastructure requirements.

Summary

• Introduction to the concept and importance of data warehouses.
• Importance of ETL processes and tools.
• Difference in approaches between large and small companies.