1. HOME
  2. Readme
  4. 1. Fundamentals
    1. 1.1. Terms to Know
    2. 1.2. Jobs
    3. 1.3. Skills needed
    4. 1.4. Application Tiers
    5. 1.5. Operational Database
    6. 1.6. What is a Data Warehouse
      1. 1.6.1. Data Architecture
      2. 1.6.2. Problem Statement
      3. 1.6.3. Key Features
      4. 1.6.4. Need for DW
      5. 1.6.5. Current State of the Art
    8. 1.7. Types of Data
    9. 1.8. Data Storage Systems
    10. 1.9. DW 1980 - Current
    11. 1.10. DW vs Data Mart
    12. 1.11. DW Architecture
      1. 1.11.1. Top-Down Approach
      2. 1.11.2. Bottom-Up Approach
      3. 1.11.3. Summary
    14. 1.12. DW Characteristic
      1. 1.12.1. Subject Oriented
      2. 1.12.2. Integrated
      3. 1.12.3. Time Variant
      4. 1.12.4. Non Volatile
    16. 1.13. Tools
    17. 1.14. Cloud vs On-Premise
    18. 1.15. Steps to design a DW
      1. 1.15.1. Gather Requirements
      2. 1.15.2. Environment
      3. 1.15.3. Data Modeling
      4. 1.15.4. ETL / ELT Solution
      5. 1.15.5. Online Analytic Processing
      6. 1.15.6. Front End
      7. 1.15.7. Query Optimization
  6. 2. RDBMS
    1. 2.1. Data Model
      1. 2.1.1. ER Model
      2. 2.1.2. Attributes
      3. 2.1.3. Keys
      4. 2.1.4. Transaction
      5. 2.1.5. ACID
    3. 2.2. Online vs Batch
    4. 2.3. DSL vs GPL
    5. 2.4. Storage Formats
    6. 2.5. File Formats
    7. 2.6. DuckDB
    8. 2.7. DuckDB Sample - 01
    9. 2.8. DuckDB Sample - 02
    10. 2.9. DuckDB - Date Dimension
    11. 2.10. SQLBolt
  8. 3. Cloud
    1. 3.1. Overview
    2. 3.2. Types
    3. 3.3. Challenges
    4. 3.4. AWS
      1. 3.4.1. AWS Global Infra
      2. 3.4.2. EC2
      3. 3.4.3. S3
      4. 3.4.4. IAM
      5. 3.4.5. CloudShell
    6. 3.5. Terraform
  10. 4. Data Architecture
    1. 4.1. Medallion Architecture
    2. 4.2. Bronze to Silver
    3. 4.3. Silver to Gold
    4. 4.4. Sample Data from Raw to Silver
    5. 4.5. Spark Intro
    6. 4.6. Delta
  12. 5. Data Warehousing Concepts
    1. 5.1. Dimensional Modelling
      1. 5.1.1. Star Schema
      2. 5.1.2. Galaxy Schema
      3. 5.1.3. Snowflake Schema
      4. 5.1.4. Starflake Schema
      5. 5.1.5. Star vs Snowflake
      6. 5.1.6. GRAIN
      7. 5.1.7. Multi-Fact Star Schema
      8. 5.1.8. Vertabelo Tool
    3. 5.2. Dimension - Fact
    4. 5.3. Sample Excercise
    5. 5.4. Keys
      1. 5.4.1. Surrogate Keys
    7. 5.5. More Examples
    8. 5.6. Master Data Management
    9. 5.7. Steps of Dimensional Modeling
    10. 5.8. Types of Dimensions
      1. 5.8.1. Date Dimension Table
      2. 5.8.2. Degenerate Dimension
      3. 5.8.3. Junk Dimension
      4. 5.8.4. Static Dimension
      5. 5.8.5. Conformed Dimensions
      6. 5.8.6. SCD
        1. 5.8.6.1. SCD - Type 0
        2. 5.8.6.2. SCD - Type 1
        3. 5.8.6.3. SCD - Type 2
        4. 5.8.6.4. SCD - Type 3
        5. 5.8.6.5. SCD - Type 4
        6. 5.8.6.6. SCD - Type 6
        7. 5.8.6.7. SCD - Type 5 - Fun Fact
      8. 5.8.7. Role Playing Dimension
      9. 5.8.8. Conformed vs Role Playing
      10. 5.8.9. Shrunken Dimension
      11. 5.8.10. Swappable Dimension
      12. 5.8.11. Step Dimension
      13. 5.8.12. Temporal
    12. 5.9. Types of Facts
      1. 5.9.1. Factless
      2. 5.9.2. Transaction
      3. 5.9.3. Periodic
      4. 5.9.4. Accumulating Snapshot
      5. 5.9.5. Transaction vs Periodic vs Accumulating
      6. 5.9.6. Additive, Semi-Additive, Non-Additive
      7. 5.9.7. Periodic Snapshot vs Additive
      8. 5.9.8. Conformed Fact
  14. 6. Miscellaneous
    1. 6.1. CSV to Dimension Models Example
    2. 6.2. Data Architecture Diagram
    3. 6.3. Data Pipeline Models
    4. 6.4. New DW Concepts
    5. 6.5. Dataset Examples
    6. 6.6. Thoughts on some data

Advance Data Warehousing