Data Warehousing Tutorial

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

This log gives a simple overview of Data Warehousing, its main features, and how it's different from regular databases (DBMS). It also explains the difference between operational systems used for daily tasks and informational systems used for reporting and analysis.

• Data Warehousing
• Characteristics of Data Warehousing
• Data Warehouse vs DBMS
• Comparison of Operational and Informational Systems

Data Warehouse Architecture

In this section, we explore the architecture of a Data Warehouse, focusing on the widely used Three-Tier Architecture. We'll also examine Data Marts and Data Lakes, and conclude with a clear comparison between Data Mart, Data Lake, and Data Warehouse to understand their purposes and differences in modern data storage systems.

• Data Warehouse Architecture
  
• Three - Tier Architecture
• Data Marts
• Data Lake
• Difference between Data Mart, Data Lake, and Data Warehouse

OLAP Technology

In this section, we explore into OLAP (Online Analytical Processing) and its crucial role in Data Warehousing. We'll explore the ETL process, compare OLAP vs OLTP, and break down key OLAP operations. The section also covers the types of OLAP systems-MOLAP, ROLAP, and HOLAP-along with their differences and implementation strategies for effective analytical processing.

• 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 Modelling

We focus on Data Warehouse Modelling, starting with an introduction to how data is structured for analysis. We'll explore the Multidimensional Data Model, explaining the roles of Fact Tables and Dimension Tables, and how they differ. Then, we'll examine popular schema models like the Star Schema and Snowflake Schema, comparing their structures. Finally, we'll look at Concept Hierarchies used for organizing data at different levels of abstraction.

• Introduction to Data Warehouse Modelling
• 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

This topic covers Data Transformation, a vital part of data preprocessing that improves data quality and usability. It includes techniques like Normalization, Aggregation, Discretization, and Sampling, along with methods for handling missing values and outliers. You'll also learn about Feature Selection, Feature Extraction, and how they contribute to Dimensionality Reduction for more efficient analysis.

• 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