Data Warehousing
  • Data Warehousing
  • Readme
  • Fundamentals
    • Terms to Know
    • Jobs
    • Skills needed for DW developer
    • 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
      • Bottom-Up Approach
    • Data Warehouse Characteristic
      • Subject Oriented
      • Integrated
      • Time Variant
      • Non Volatile
    • Tools
    • Cloud vs On-Premise
    • Steps to design a Data Warehouse
      • Gather Requirements
      • Environment
      • Data Modeling
      • Choosing ETL / ELT Solution
      • Online Analytic Processing
      • Front End
      • Query Optimization
    • Dataset Examples
    • Thoughts on some data
  • RDBMS
    • Data Model
      • Entity Relationship Model
      • Attributes
      • Keys
      • Transaction
      • ACID
    • Online vs Batch
    • DSL vs GPL
    • Connect to Elvis
    • SQL Concepts
      • Basic Select - 1
      • Basic Select - 2
      • UNION Operators
      • Wild Cards & Distinct
      • Group By & Having
      • Sub Queries
      • Derived Tables
      • Views
    • Practice using SQLBolt
  • Cloud
    • Overview
    • Types of Cloud Services
    • Challenges of Cloud Computing
    • AWS
      • AWS Global Infrastructure
      • EC2
      • S3
      • IAM
    • Terraform
  • Spark - Databricks
    • Storage Formats
    • File Formats
    • Medallion Architecture
    • 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
  2. Types of Facts

Conformed Fact

The fact is used in more than one fact table. (Attribute)

The conformed Fact table is a fact table that can be used across multiple Data Marts.

Suppose a company has separate fact tables for Online Sales and In-store Sales. Both these fact tables might have a "Total Sales" metric.

To ensure that this metric is a conformed fact, the company would need to make sure that "Total Sales" has the same meaning (i.e., the total revenue from sales), the same scale (e.g., in US dollars), and is calculated in the same way (e.g., quantity sold times unit price) in both fact tables.

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Last updated 1 year ago