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    Data Warehouse Concepts: Basic to Advanced concepts

    BY
    Udemy

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    Mode

    Online

    Fees

    ₹ 599 2499

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 599  ₹2,499
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Introduction

    • Welcome! Thank you for learning the Data Warehouse concepts with me!

    Brief about the Data Warehouse

    • Is Data Warehouse still relevant in the age of Big Data?
    • Why do we need a Data Warehouse?
    • What is Data Warehouse?
    • Characteristics of Data Warehouse

    Business Intelligence

    • What is Business Intelligence?
    • Business Intelligence -Extended Explanation
    • Uses of Business Intelligence
    • Tools used for (in) Business Intelligence

    Data Warehouse Architectures

    • Enterprise Architecture or Centralized Architecture
    • Federated Architecture
    • Multi-Tired Architecture
    • Components of a Data Warehouse
    • Purpose of a Staging Area in Data Warehouse Architecture - Part 1
    • Purpose of a Staging Area in Data Warehouse Architecture - Part 2
    • Advantages of Traditional warehouse
    • Limitations of Traditional Data Warehouses

    ODS - Operational Data Store

    • What is ODS?
    • Define ODS
    • Staging Vs ODS
    • Differences between ODS,DWH, OLTP, OLAP, DSS

    OLAP

    • OLAP Overview
    • OLTP Vs OLAP - Part 1_U
    • OLTP Vs OLAP - Part 2
    • OLAP Architecture - MOLAP
    • ROLAP
    • HOLAP
    • DOLAP

    Data Mart

    • What is a Data Mart?
    • Fundamental Difference between DWH and DM
    • Advantages of a Data Mart
    • Characteristics of a Data Mart
    • Disadvantages of a Data Mart
    • Mistakes and MisConceptions of a Data Mart

    Metadata

    • Overview of Metadata
    • Benefits of Metadata
    • Types of Metadata
    • Projects on Metadata
    • Best Practices for Metadata Setup
    • Summary

    Data Modeling

    • What is Data Modeling?
    • Data Modeling Techniques

    Entity Relation Data Model

    • ER - (Entity Relation) Data Model
    • ER Data Model - What is Entity?
    • ER Data Model - Types of Entities - Part 1
    • ER Data Model - Types of Entities - Part 2
    • ER Data Model - Attributes
    • ER Data Model - Types of Attributes
    • ER Data Model - Entity-Set and Keys
    • ER Data Model - Identifier
    • ER Data Model - Relationship
    • ER Data Model - Notation
    • ER Data Model - Logical Data Model
    • ER Data Model - Moving from Logical Data Model to Physical Data Model
    • ER Data Model - Differences between CDM, LDM and PDM
    • ER Data Model - Disadvantages

    Dimensional Model

    • What is Dimension Modelling?
    • Benefits of Dimensional Modelling
    • What is a Dimension?
    • What is a Fact?
    • Additive Facts
    • Semi Additive Facts
    • Non-Additive Facts
    • FactLess Facts
    • What is a Surrogate key?
    • Star Schema
    • SnowFlake Schema
    • Galaxy Schema or Fact Constellation Schema
    • Differences between Star Schema and SnowFlake Schema?
    • Conformed Dimension
    • Junk Dimension
    • Degenerate Dimension
    • Slowly Changing Dimensions - Intro and Example Creation
    • Slowly Changing Dimensions - Type 1, 2 and 3
    • Slowly Changing Dimensions - Summary
    • Step by Step approach to set up the Dimensional Model using a retail case study
    • ER Model Vs Dimensional Model

    DWH Indexes

    • What is an Index?
    • Bitmap Index
    • B-Tree index
    • Bitmap Index Vs B Tree Index

    Data Integration and ETL

    • What is Data Integration?
    • What is ETL?
    • Common Questions and Summary

    ETL Vs ELT

    • ETL - Explained
    • ELT - Explained
    • ETL Vs ELT

    ETL - Extraction Transformation & Loading

    • Build Vs Buy
    • ETL Tools for Data Warehouses
    • Extraction Methods in Data Warehouses

    Typical Roles in DWH Project

    • Project Sponsor
    • Project Manager
    • Functional Analyst or Business Analyst
    • SME - Subject Matter Expert
    • DW BI Architect
    • Data Modeler
    • DWH Tech Admin
    • ETL Developers
    • BI OLAP Developers
    • ETL Testers/QA Group
    • DB UNIX Network Admins
    • Data Architect, Data Warehouse Architect, BI Architect and Solution Architect
    • Final Note about the Roles

    DW/BI/ETL Implementation Approach

    • Different phases in DW/BI/ETL Implementation Approach
    • Knowledge Capture Sessions
    • Requirements
    • Architecture phases
    • Data Model/Database
    • ETL Phase
    • Data Access Phase
    • Data Access Types - Selection
    • Data Access Types - Drilling Down
    • Data Access Types - Exception Reporting
    • Data Access Types - Calculations
    • Data Access Types - Graphics and Visualization
    • Data Access Types -Data Entry Options
    • Data Access Types - Customization
    • Data Access Types - WebBased Reporting
    • Data Access Types - BroadCasting
    • Deploy
    • Iterative Approach

    Retired Lectures

    • ETL Vs ELT

    Instructors

    Mr Sid Inf
    Instructor
    Freelancer

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