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Data Warehouse Concepts: Basic to Advanced concepts

Data Warehouse Concepts: Learn the in BI/Data Warehouse/BIG DATA Concepts from scratch and become an expert.

Created by Inf Sid, Last Updated 23-Oct-2019, Language:English

Description

In this course, you will learn all the concepts and terminologies related to the Data Warehouse , such as the OLTP, OLAP, Dimensions, Facts and much more, along with other concepts related to it such as what is meant by Start Schema, Snow flake Schema, other options available and their differences. It also explains how the data is managed with in the Data Warehouse and explains the process of reading and writing data onto the Warehouse. Later in the course you would also learn the basics of Data Modelling and how to start with it logically and physically. You would also learn all the concepts related to Facts, Dimensions, Aggregations and commonly used techniques of ETL. 

Upon completion of this course, you would have a clear idea about, all the concepts related to the Data Warehouse, that should be sufficient to help you start off with the next step of becoming an ETL developer or Administering the Data warehouse environment with the help of various tools.  


All the Best and Happy Learning !

Requirements

  • Basic understanding of the IT (Software) industry
  • Familiar with the basic concept of Database/RDBMS.

What Will I Get ?

  • Brief about the Data Warehouse and how the concept came into existence
  • Business Intelligence Concepts
  • Data Warehouse Architectures
  • ODS - Operational Data Store
  • OLAP - Online Analytical Processing
  • Data Marts
  • Metadata
  • Data Modeling
  • Entity Relationship Model
  • Dimensional Model
  • Data Integration & ETL
  • ETL vs ELT
  • ETL - Extraction Transformation & Loading
  • Typical Roles in a Data Warehouse Project
  • DW/BI/ETL Implementation Approach

Course Content

  • Brief about the Data warehouse 4 Lectures 00:21:22

    • Is Data Warehouse still relevant in the age of Big Data?

      Preview
      00:04:25

    • Why do we need a Data Warehouse?

      Preview
      00:05:26

    • What is a Data Warehouse?

      Preview
      00:05:48

    • Characteristics of a Data Warehouse

      Preview
      00:05:43
  • Business Intelligence 4 Lectures 00:23:37

    • What is Business Intelligence?

      00:05:37

    • Business Intelligence -Extended Explanation

      00:03:34

    • Uses of Business Intelligence

      00:08:02

    • Tools used for (in) Business Intelligence

      00:06:24
  • Data Warehouse Architectures 8 Lectures 00:31:18

    • Enterprise Architecture or Centralized Architecture

      00:04:08

    • Federated Architecture

      00:02:49

    • Multi-Tired Architecture

      00:03:13

    • Components of a Data Warehouse

      00:03:57

    • Purpose of a Staging Area in Data Warehouse Architecture - Part 1

      00:04:49

    • Purpose of a Staging Area in Data Warehouse Architecture - Part 2

      00:03:41

    • Advantages of Traditional warehouse

      00:02:33

    • Limitations of Traditional Data Warehouses

      00:06:08
  • ODS - Operational Data Store 4 Lectures 00:16:37

    • What is ODS?

      00:02:26

    • Define ODS

      00:07:40

    • Features and Benefits of ODS

      00:02:24

    • Differences between ODS,DWH, OLTP, OLAP, DSS

      00:04:07
  • OLAP 7 Lectures 00:28:51

    • OLAP Overview

      00:05:54

    • OLTP Vs OLAP - Part 1

      00:04:05

    • OLTP Vs OLAP - Part 2

      00:05:31

    • OLAP Architecture - MOLAP

      00:05:56

    • ROLAP

      00:03:35

    • HOLAP

      00:02:20

    • DOLAP

      00:01:30
  • Data Mart 6 Lectures 00:13:52

    • What is a Data Mart?

      00:01:40

    • Fundamental Difference between DWH and DM

      00:00:40

    • Advantages of a Data Mart

      00:02:46

    • Characteristics of a Data Mart

      00:03:37

    • Disadvantages of a Data Mart

      00:03:01

    • Mistakes and MisConceptions of a Data Mart

      00:02:08
  • Metadata 6 Lectures 00:19:30

    • Overview of Metadata

      00:01:50

    • Benefits of Metadata

      00:01:47

    • Types of Metadata

      00:05:38

    • Projects on Metadata

      00:05:28

    • Best Practices for Metadata Setup

      00:01:36

    • Summary

      00:03:11
  • Data Modeling 2 Lectures 00:05:53

    • What is Data Modeling?

      00:02:11

    • Data Modeling Techniques

      00:03:42
  • Entity Relational Data Model 14 Lectures 00:35:45

    • ER - (Entity Relation) Data Model

      00:03:37

    • ER Data Model - What is Entity?

      00:02:01

    • ER Data Model - Types of Entities - Part 1

      00:03:56

    • ER Data Model - Types of Entities - Part 2

      00:01:49

    • ER Data Model - Attributes

      00:01:54

    • ER Data Model - Types of Attributes

      00:03:59

    • ER Data Model - Entity-Set and Keys

      00:02:42

    • ER Data Model - Identifier

      00:01:53

    • ER Data Model - Relationship

      00:01:15

    • ER Data Model - Notation

      00:02:34

    • ER Data Model - Logical Data Model

      00:01:30

    • ER Data Model - Moving from Logical Data Model to Physical Data Model

      00:02:14

    • ER Data Model - Differences between CDM, LDM and PDM

      00:03:06

    • ER Data Model - Disadvantages

      00:03:15
  • Dimensional Model 21 Lectures 01:24:27

    • What is Dimension Modelling?

      00:04:38

    • Benefits of Dimensional Modelling

      00:01:52

    • What is a Dimension?

      00:02:36

    • What is a Fact?

      00:02:00

    • Additive Facts

      00:01:45

    • Semi Additive Facts

      00:02:23

    • Non-Additive Facts

      00:01:26

    • FactLess Facts

      00:02:26

    • What is a Surrogate key?

      00:03:45

    • Star Schema

      00:04:54

    • SnowFlake Schema

      00:03:22

    • Galaxy Schema or Fact Constellation Schema

      00:02:20

    • Differences between Star Schema and SnowFlake Schema?

      00:04:55

    • Conformed Dimension

      00:06:17

    • Junk Dimension

      00:03:12

    • Degenerate Dimension

      00:03:36

    • Slowly Changing Dimensions - Intro and Example Creation

      00:05:35

    • Slowly Changing Dimensions - Type 1, 2 and 3

      00:12:14

    • Slowly Changing Dimensions - Summary

      00:03:05

    • Step by Step approach to set up the Dimensional Model using a retail case study

      00:06:44

    • ER Model Vs Dimensional Model

      00:05:22
  • DWH Indexes 4 Lectures 00:10:59

    • What is an Index?

      00:02:04

    • Bitmap Index

      00:03:46

    • B-Tree index

      00:01:49

    • Bitmap Index Vs B Tree Index

      00:03:20
  • Data Integration and ETL 3 Lectures 00:13:20

    • What is Data Integration?

      00:06:49

    • What is ETL?

      00:03:49

    • Common Questions and Summary

      00:02:42
  • ETL Vs ELT 3 Lectures 00:13:45

    • ETL - Explained

      00:06:03

    • ELT - Explained

      00:05:24

    • ETL Vs ELT

      00:02:18
  • ETL - Extraction Transformation & Loading 3 Lectures 00:12:48

    • Build Vs Buy

      00:05:10

    • ETL Tools for Data Warehouses

      00:01:56

    • Extraction Methods in Data Warehouses

      00:05:42
  • Typical Roles In DWH Project 13 Lectures 00:44:18

    • Project Sponsor

      00:03:24

    • Project Manager

      00:01:46

    • Functional Analyst or Business Analyst

      00:02:53

    • SME - Subject Matter Expert

      00:04:17

    • DW BI Architect

      00:03:07

    • Data Modeler

      00:08:59

    • DWH Tech Admin

      00:01:20

    • ETL Developers

      00:01:56

    • BI OLAP Developers

      00:01:29

    • ETL Testers/QA Group

      00:01:58

    • DB UNIX Network Admins

      00:00:56

    • Data Architect, Data Warehouse Architect, BI Architect and Solution Architect

      00:09:57

    • Final Note about the Roles

      00:02:16
  • DW/BI/ETL Implementation Approach 18 Lectures 00:39:48

    • Different phases in DW/BI/ETL Implementation Approach

      00:01:51

    • Knowledge Capture Sessions

      00:03:34

    • Requirements

      00:07:21

    • Architecture phases

      00:04:48

    • Data Model/Database

      00:01:35

    • ETL Phase

      00:02:43

    • Data Access Phase

      00:02:10

    • Data Access Types - Selection

      00:01:37

    • Data Access Types - Drilling Down

      00:00:58

    • Data Access Types - Exception Reporting

      00:00:36

    • Data Access Types - Calculations

      00:01:26

    • Data Access Types - Graphics and Visualization

      00:00:58

    • Data Access Types -Data Entry Options

      00:02:04

    • Data Access Types - Customization

      00:01:00

    • Data Access Types - WebBased Reporting

      00:00:56

    • Data Access Types - BroadCasting

      00:01:04

    • Deploy

      00:01:42

    • Iterative Approach

      00:03:25

Inf Sid

Data Management Consultant

Business Intelligence Consultant and Trainer with 16+ years of extensive work experience on various client engagements. Delivered many large data warehousing projects and trained numerous professionals on business intelligence technologies. Extensively worked on all facets of data warehousing including requirement gathering, gap analysis, database design, data integration, data modeling, enterprise reporting, data analytics, data quality, data visualization, OLAP. Has worked on broad range of business verticals and hold exceptional expertise on  various ETL tools like Informatica Powercenter, SSIS, ODI and IDQ, Data Virtualization, DVO, MDM.

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