โ
Data Warehousing Basics ๐ข๐ฆ
๐ A Data Warehouse is a central repository used to store large volumes of historical data from multiple sources for reporting and analysis.
It is designed for:
โข โ Business Intelligence BI
โข โ Reporting
โข โ Data Analytics
โข โ Decision-making
๐น 1. What is a Data Warehouse?
A Data Warehouse collects data from different systems into one centralized location.
Example
A retail company stores data from:
โข โ Sales system
โข โ Inventory system
โข โ Customer database
โข โ Finance system
All this data is combined into a Data Warehouse for analysis.
๐ฅ 2. Why Do We Need a Data Warehouse?
โข โ Centralized data storage
โข โ Faster reporting
โข โ Historical data analysis
โข โ Better business decisions
๐น 3. Data Warehouse Architecture โญ
Data Sources
โ
ETL Extract, Transform, Load
โ
Data Warehouse
โ
Reports & Dashboards
๐น 4. What is ETL?
ETL stands for:
โ
Extract
Collect data from different sources.
โ
Transform
Clean, format, and prepare the data.
โ
Load
Store the transformed data in the Data Warehouse.
๐น 5. OLTP vs OLAP โญ
OLTP | OLAP
---|---
Daily transactions | Data analysis
Fast inserts & updates | Fast reporting
Current data | Historical data
Examples:
โข OLTP: Banking transactions, online shopping orders
โข OLAP: Sales reports, yearly revenue analysis
๐น 6. Star Schema โญ
The most common Data Warehouse schema.
It contains:
โญ Fact Table
Stores measurable values
Example: Sales Amount, Quantity
โญ Dimension Tables
Store descriptive information
Example: Customer, Product, Date
๐น 7. Snowflake Schema
Similar to Star Schema but with normalized dimension tables.
๐ Uses more tables and relationships.
๐น 8. Popular Data Warehousing Tools
โข โ Snowflake
โข โ Google BigQuery
โข โ Amazon Redshift
โข โ Azure Synapse Analytics
๐น 9. Why Data Warehousing is Important?
โข โ Stores large amounts of data
โข โ Supports business intelligence
โข โ Enables faster analytics
โข โ Frequently asked in interviews
๐ฏ Today's Goal
โข โ Understand Data Warehouse concepts
โข โ Learn ETL process
โข โ Differentiate OLTP vs OLAP
โข โ Understand Star Schema & Fact/Dimension tables
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