Data Warehousing Tutorial
Last Updated :
19 Mar, 2025
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Data warehousing refers to the process of collecting, storing, and managing data from different sources in a centralized repository. It allows businesses to analyze historical data and make informed decisions. The data is structured in a way that makes it easy to query and generate reports.
- A data warehouse consolidates data from multiple sources.
- It helps businesses track historical trends and performance.
- Facilitates complex queries and analysis for decision-making.
- Enables efficient reporting and business intelligence.

Introduction to Data Warehousing
- Data Warehousing
- Characteristics of Data Warehousing
- Data Warehouse vs DBMS
- Comparison of Operational and Informational Systems
Data Warehouse Architecture
- Data Warehouse Architecture
- Three - Tier Architecture
- Data Marts
- Data Lake
- Difference between Data Mart, Data Lake, and Data Warehouse
OLAP Technology
- OLAP in Data Warehousing
- OLAP vs OLTP
- ETL Process
- ETL vs ELT
- OLAP Operations
- Types of OLAP Systems
- MOLAP (Multidimensional OLAP)
- ROLAP (Relational OLAP)
- HOLAP (Hybrid OLAP)
- ROLAP vs MOLAP
- Difference between ROLAP, MOLAP and HOLAP
- OLAP Implementation
Data Warehouse Modeling
- Introduction to Data Warehouse Modeling
- Multidimensional Data Model
- Fact Table
- Dimension Table
- Fact Tables vs Dimension Tables
- Data Warehouse Schema Models
- Star Schema
- Snowflake Schema
- Star Schema vs Snowflake Schema
- Concept Hierarchy
Data Transformation
- Introduction to Data Transformation
- Types of Data Transformation
- Data Normalization
- Aggregation
- Discretization
- Data Sampling
- Handling Missing Values
- Handling Outliers
- Feature Selection
- Feature Extraction
- Difference between Feature Selection and Feature Extraction
- Dimensionality Reduction
Miscellaneous Topics
- Measures - Categorization and Computation
- Data Warehouse Implementation
- Performance Optimization in Data Warehousing
- Data Warehouse Tools and Technologies