Data Warehousing
  • Data Warehousing
  • Readme
  • Fundamentals
    • Terms to Know
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    • Application Tiers
    • Operational Database
    • What is a Data Warehouse
      • Typical Data Architecture
      • Problem Statement
      • Features of Data Warehouse
      • Need for Data Warehouse
      • Current State of the Art
    • Activities of Data Science
    • Types of Data
    • Data Storage Systems
    • Data Warehouse 1980 - Current
    • Data Warehouse vs Data Mart
    • Data Warehouse Architecture
      • Top-Down Approach
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    • Data Warehouse Characteristic
      • Subject Oriented
      • Integrated
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      • Non Volatile
    • Tools
    • Cloud vs On-Premise
    • Steps to design a Data Warehouse
      • Gather Requirements
      • Environment
      • Data Modeling
      • Choosing ETL / ELT Solution
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    • Dataset Examples
    • Thoughts on some data
  • RDBMS
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    • SQL Concepts
      • Basic Select - 1
      • Basic Select - 2
      • UNION Operators
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    • Storage Formats
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    • Delta
  • Data Warehousing Concepts
    • Dimensional Modelling
      • Star Schema
      • Galaxy Schema
      • Snowflake Schema
      • Starflake Schema
      • Star vs Snowflake
      • GRAIN
      • Multi-Fact Star Schema
      • Vertabelo Tool
    • Dimension - Fact
    • Sample Excercise
    • Keys
      • Why Surrogate Keys are Important
    • More Examples
    • Master Data Management
    • Steps of Dimensional Modeling
    • Types of Dimensions
      • Date Dimension Table
      • Degenerate Dimension
      • Junk Dimension
      • Static Dimension
      • Conformed Dimensions
      • Slowly Changing Dimensions
        • SCD - Type 0
        • SCD - Type 1
        • SCD - Type 2
        • SCD - Type 3
        • SCD - Type 4
        • SCD - Type 6
        • SCD - Type 5 - Fun Fact
      • Role Playing Dimension
      • Conformed vs Role Playing
      • Shrunken Dimension
      • Swappable Dimension
      • Step Dimension
    • Types of Facts
      • Factless Fact Table
      • Transaction Fact
      • Periodic Fact
      • Accumulating Snapshot Fact Table
      • Transaction vs Periodic vs Accumulating
      • Additive, Semi-Additive, Non-Additive
      • Periodic Snapshot vs Additive
      • Conformed Fact
    • Sample Data Architecture Diagram
    • Data Pipeline Models
    • New DW Concepts
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  1. Data Warehousing Concepts

Master Data Management

Master Data Management (MDM) refers to creating and managing data that an organization must have as a single master copy, called the master data.

  • State

  • Customers

  • Vendors

  • Products

It is the single source of the truth.

MDM is not Data Warehouse but is closely related to Data Warehouse.

Different Goals: MDM is to create and maintain a single source of truth. Whereas in DW, Sales Customer vs. Marketing Customer may differ and not follow a single source of truth.

Types of Data: MDM contains data that doesn't change, mainly Dimensions. At the same time, DW has both Dimensions and Facts.

Reporting Needs: Data Warehousing's priority is to address end-user requirements. MDM's priority is ensuring its follows data governance, quality, and complaint.

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Last updated 2 years ago