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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.
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Data Warehousing

Introduction to Data Warehousing

  1. Data Warehousing
  2. Characteristics of Data Warehousing
  3. Data Warehouse vs DBMS
  4. Comparison of Operational and Informational Systems

Data Warehouse Architecture

  1. Data Warehouse Architecture
  2. Three - Tier Architecture
  3. Data Marts
  4. Data Lake
  5. Difference between Data Mart, Data Lake, and Data Warehouse

OLAP Technology

  1. OLAP in Data Warehousing
  2. OLAP vs OLTP
  3. ETL Process
  4. ETL vs ELT
  5. OLAP Operations
  6. Types of OLAP Systems
  7. MOLAP (Multidimensional OLAP)
  8. ROLAP (Relational OLAP)
  9. HOLAP (Hybrid OLAP)
  10. ROLAP vs MOLAP
  11. Difference between ROLAP, MOLAP and HOLAP
  12. OLAP Implementation

Data Warehouse Modeling

  1. Introduction to Data Warehouse Modeling
  2. Multidimensional Data Model
  3. Fact Table
  4. Dimension Table
  5. Fact Tables vs Dimension Tables
  6. Data Warehouse Schema Models
  7. Star Schema
  8. Snowflake Schema
  9. Star Schema vs Snowflake Schema
  10. Concept Hierarchy

Data Transformation

  1. Introduction to Data Transformation
  2. Types of Data Transformation
  3. Data Normalization
  4. Aggregation
  5. Discretization
  6. Data Sampling
  7. Handling Missing Values
  8. Handling Outliers
  9. Feature Selection
  10. Feature Extraction
  11. Difference between Feature Selection and Feature Extraction
  12. Dimensionality Reduction

Miscellaneous Topics

  1. Measures - Categorization and Computation
  2. Data Warehouse Implementation
  3. Performance Optimization in Data Warehousing
  4. Data Warehouse Tools and Technologies

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