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Analytics Engineering Bootcamp

Data Warehouse

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Business Analytics & Intelligence

Analytics Engineering Bootcamp

Become an Analytics Engineer expert with just ONE course. Learn Data Modelling, dbt, Google Bigquery & many more!

4.5

(1780)

14090 enrolled on this course

Last updated 11/2021

Rahul Prasad

₹ 3099.0

₹ 4599.0

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30-Day Money-Back Guarantee

Lessons

Quizzes

Duration

13 Hours

Skill level

Beginner

Language

English

Certificate

Yes

Full lifetime access

Yes

Description

Welcome to the Analytics Engineering Bootcamp course.
the only course you need to become an amazing Analytics Engineer.
This complete Analytics Engineering Bootcamp will take you step-by-step through engaging and fun lectures and teach you everything you need to know on how to succeed as an Analytics Engineer. Throughout this course you’ll get an in depth insight into all the various tools, technologies and modelling concepts.
Students will learn how to design and implement a Data Warehouse solution using DBT (Data build tool) & BigQuery.
Each section contains scenario based quiz questions that help solidify key learning objectives for each concept & theory..
By the end of the course, you'll learn and get really good understanding of:
Differences between database and a data warehouse
Concepts between OLTP & OLAP systems
Normalisation & De-Normalisation methods
Data Modelling methodologies such as (Inmon, Kimball, Data Vault & OBT)
Difference between ETL & ELT
Data modelling techniques especially using
dbt
Hands-on experience building dimensional data warehouse
RECENT UPDATES:
Mar2023 - Updated Glossary and added more contents
Mar2024 - New: dbt Power User accelerated development lectures (Including usage of Data Pilot, Generative AI driven workflow assistant)
Who this course is for:
Data Analyst, BI Analysts or Data Warehouse developers who are looking to become Analytics Engineers or looking to improve existing skills
For data professionals who wants to get a refresher on all the concepts and terms surrounding OLTP & OLAP systems
Students or recent graduates who are looking to get a job as an Analytics Engineer
Anyone who is interested in Analytics Engineer Career Path
Who this course is for:
Anyone who is interested in becoming an Analytics Engineer
Anyone who want's to understand more about data modelling and data transformation

What you'll learn

Learn all the skill sets that is required to become an Analytics Engineer

In-depth understanding of data modelling techniques

Ability to participate in architectural decision making and be able to create one

Data modelling techniques using DBT

Learn hands-on skills required to build a Data Warehouse from scratch

Boost your resume with most in-demand Analytics Engineer skills

Design & Implement a data warehouse

Create Data Warehouse Architecture

Design Conceptual, Logical & Physical Models

Learn various modelling methodologies (Inmon, Kimball, Data Vault, OBT)

Apply principles of dimensional data modeling in a hands-on

Learn all the concepts and terms such as the OLTP, OLAP, Facts, Dimensions, Star Schema, Snowflake Schema

Requirements

You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
No previous design experience is needed.
No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures

Expand All Sections

1-Introduction

1.1-Introduction

1.2-Course Overview

1.3-How to get the best out of this course

1.4-Resources

2-What is a database?

2.1-Database Introduction

2.2-Database definition

2.3-SQL Example

2.4-Database Management System (DBMS)

2.5-Sheets vs Database

2.6-OLTP

2.7-OLTP ACID

2.8-OLAP

2.9-OLTP vs OLAP Summary

2.10-NoSQL Introduction

2.11-Key Value Store

2.12-Document Store

2.13-Wide Columns

2.14-Graph Database

2.15-Search Engines

2.16-SQL vs NoSQL

2.17-On-Prem vs Cloud

2.18-Quiz

3-What is a data warehouse?

3.1-Data Warehouse Introduction

3.2-Data Warehouse Definition

3.3-Data Warehouse Benefits

3.4-Data Warehouse Architecture

3.5-Data Source

3.6-Data Lake

3.7-Data Warehouse Layer

3.8-Business Intelligence Introduction

3.9-Business Intelligence Tools

3.10-ETL - ELT Introduction

3.11-ETL

3.12-ELT

3.13-ETL vs ELT

3.14-Quiz

4-Data Modelling & ERD Notations

4.1-Data Modelling & Entity Relationship Diagram (ERD) Introduction

4.2-Data Modelling Overview

4.3-ERD Overview

4.4-Entity Attributes Relationships

4.5-Steps to Create an ERD

4.6-Build ERD using Chen's Notation Style

4.7-Build ERD using Information Engineering Notation Style

4.8-Data Modelling Concepts

4.9-Different Type of Keys

4.10-Recommended Tools for Creating ERD

4.11-Quiz

5-Normalisation & Denormalisation

5.1-What is Normalisation?

5.2-1st Normal Form

5.3-2nd Normal Form

5.4-3rd Normal Form

5.5-Pros & Cons of Normalised Model

5.6-What is De-Normalisation?

5.7-De-Normalisation Techniques

5.8-Pros & Cons of De-Normalised Model

5.9-Quiz

6-Data Warehouse Design Methodologies

6.1-Data Warehouse Design Methodologies Introduction

6.2-Inmon Methodology

6.3-Corporate Information Factory (CIF) Architecture Explained

6.4-Inmon Architecture

6.5-Pros & Cons of Inmon Methodology

6.6-Kimball Methodology

6.7-Processes of Kimball Methodology

6.8-Kimball Architecture

6.9-Pros & Cons of Kimball Methodology

6.10-Inmon vs Kimball

6.11-Hybrid Architecture

6.12-Data Vault Methodology Introduction

6.13-Data Vault Components

6.14-Data Vault Architecture & Example

6.15-Pros & Cons of Data Vault

6.16-Inmon vs Kimball vs Data Vault

6.17-One Big Table (OBT) / Wide Table

6.18-Pros & Cons of OBT

6.19-Data Modelling Then, Now & Next

6.20-Quiz

7-Dimensional Modelling

7.1-Dimensional Modelling Introduction

7.2-What is Dimensional Modelling?

7.3-Data Warehouse LifeCycle Overview

7.4-Program/Project Planning

7.5-Requirement Gathering

7.6-Concept & Steps of Dimensional Modelling

7.7-Select Business Process & Declare the Grain

7.8-Dimensions (Types)

7.9-Conformed Dimensions

7.10-Junk Dimensions

7.11-Degenerate Dimensions

7.12-Role Playing Dimensions

7.13-Slowly Changing Dimensions (SCD) - Intro

7.14-Type 0 - SCD (Slowly Changing Dimensions)

7.15-Type 1 - SCD (Slowly Changing Dimensions)

7.16-Type 2 - SCD (Slowly Changing Dimensions)

7.17-Type 3 - SCD (Slowly Changing Dimensions)

7.18-Type 4 - SCD (Slowly Changing Dimensions)

7.19-SCD - Store as Snapshots

7.20-Bridge Tables

7.21-Facts

7.22-Additive Facts

7.23-Semi-Additive Facts

7.24-Non-Additive Facts

7.25-Transaction Facts Tables

7.26-Periodic Facts Tables

7.27-Accumulative Facts Tables

7.28-Star Schema

7.29-Snowflake Schema

7.30-Quiz

8-Setting up Environments

8.1-BigQuery Setup Introduction

8.2-BigQuery Tables Setup using CSV

8.3-BigQuery Tables Setup Using SQL Script

8.4-Setting up WSL2 for Windows

8.5-Git Repository Setup

8.6-dbt setup & Installation

9-(Hands-on dbt) Building dimensional data warehouse

9.1-Introduction

9.2-Hands-on overview

9.3-Use-Case Introduction

9.4-Use-Case Detailed Discussion

9.5-Requirements Gathering

9.6-Data Profiling - Introduction

9.7-Data Profiling - Completed

9.8-AE Workbook - Walkthrough

9.9-Bus Matrix - High Level Entities

9.10-Conceptual Model

9.11-Architecture Design

9.12-Dimensional Modelling Introduction

9.13-Bus Matrix Detailed

9.14-Source to Target Mapping (Source to BQ Data Lake)

9.15-Source to Target Mapping (BQ Data Lake to Staging)

9.16-Dimensional Model (Attributes & Measures)

9.17-Source to Target Mapping (Data Lake to Data Warehouse)

9.18-Source to Target Mapping (Data Warehouse to OBT)

9.19-Logical Model Design

9.20-Physical Model Design

9.21-dbt overview

9.22-Physical Implementation (Staging Layer)

9.23-Physical Implementation (Staging Layer) Cont.

9.24-Physical Implementation Dim Tables (Data Warehouse Layer)

9.25-Physical Implementation Fact Tables (Data Warehouse Layer)

9.26-Physical Implementation (Analytics OBT)

9.27-Debugging (dbt)

9.28-Adding Tests (dbt)

9.29-Hands-on Complete

10-Accelerate dbt Development with Power User for dbt (DataPilot)

10.1-Introduction to dbt Power User

10.2-dbt Power User pre-requisites

10.3-Installation and Configuation

10.4-Generate dbt Models from source file or SQL

10.5-Query Translation

10.6-Query Explanation

10.7-dbt Actions tool and Query results preview

10.8-Update dbt Model

10.9-Project Governance

10.10-Write dbt Tests Automatically

10.11-Impact Analysis with Column Lineage

10.12-Documentation Generation

10.13-Collaboration Workflow

10.14-Defer to prod

11-Glossary

11.1-Glossary

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