Data warehouse modeling tutorial

  • Basic elements of data warehouse

    Data warehouse modeling includes:

    Top Down / Requirements Driven Approach.Fact Tables and Dimension Tables.Multidimensional Model/Star Schema.Support Roll Up, Drill Down, and Pivot Analysis.Time Phased / Temporal Data.Operational Logical and Physical Data Models.Normalization and Denormalization..

  • Basic elements of data warehouse

    Data Warehouse Data Modeling
    First, you'll cover the basics of data modeling by learning what a fact and a dimension table are and how you use them in the star and snowflake schemes.
    Then, you'll review how to create a data model using Kimball's four-step process and how to deal with slowly changing dimensions..

  • What are the techniques of data warehousing modeling?

    There are two main types of data warehouse modeling techniques: dimensional modeling and relational modeling.
    Dimensional modeling uses a star or snowflake schema to represent the data as facts and dimensions.
    Facts are numerical measures of business events, such as sales, orders, or transactions..

  • What is data warehouse Modelling?

    Data warehouse modeling is the process of designing and organizing your data models within your data warehouse platform.
    The design and organization process consists of setting up the appropriate databases and schemas so that the data can be transformed and then stored in a way that makes sense to the end user..

  • What is data warehouse tutorial?

    A data warehouse is constructed by integrating data from multiple heterogeneous sources.
    It supports analytical reporting, structured and/or ad hoc queries and decision making.
    This tutorial adopts a step-by-step approach to explain all the necessary concepts of data warehousing..

Data warehouse modeling is the process of designing the schemas of the detailed and summarized information of the data warehouse. The goal of data warehouse 

Introduction

In December 2020 the first ever Coalesce conference was organized by Fishtown Analytics, the creators of dbt

Raw Data Schema

Gathering data from different sources Before discussing the ideas mentioned in the session and the community discussions

Staging

Making the sources more usable Now that all our sources are available in the data lake it is time to start the T in ELT : Transformation

Reporting Models

Simple Metrics and Questions Now that our data is cleaned in the staging schema

Conclusion

In this article, we have introduced the first steps of structuring and modelling a data warehouse

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