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

Periodic Snapshot vs Additive

Periodic snapshot fact and additive fact are different concepts, although they may appear similar at first glance.

A periodic snapshot fact table is designed to capture a point-in-time snapshot of some measure at regular intervals, typically periodically, such as daily, weekly, monthly, or quarterly.

An additive fact represents a measure that can be aggregated across all dimensions in a fact table. For example, sales revenue is an additive fact because it can be summed up across all dimensions such as time, product, region, etc.

Additive Fact
Periodic Snapshot Fact

Definition

Represents a measure that can be aggregated across all dimensions in a fact table

Captures a point-in-time snapshot of some measure at regular intervals

Granularity

Can be at any level of granularity

Usually at a higher level of granularity such as daily, weekly, monthly, or quarterly

Aggregation

Can be summed up across all dimensions in the fact table

Captures the total value of the measure at a specific point in time

Example

Sales revenue, profit, number of clicks on a banner ad

Monthly sales revenue, quarterly website visits, daily customer support tickets

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Last updated 2 years ago