Analytics Engineering Bootcamp

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

4.51 (1179 reviews)
Udemy
platform
English
language
Data & Analytics
category
instructor
Analytics Engineering Bootcamp
9,401
students
11 hours
content
Mar 2023
last update
$84.99
regular price

What you will 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

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


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

Content

Introduction

Introduction
Course Overview
How to get the best out of this course
Resources

What is a database?

Database Introduction
Database definition
SQL Example
Database Management System (DBMS)
Sheets vs Database
OLTP
OLTP ACID
OLAP
OLTP vs OLAP Summary
NoSQL Introduction
Key Value Store
Document Store
Wide Columns
Graph Database
Search Engines
SQL vs NoSQL
On-Prem vs Cloud
Quiz

What is a data warehouse?

Data Warehouse Introduction
Data Warehouse Definition
Data Warehouse Benefits
Data Warehouse Architecture
Data Source
Data Lake
Data Warehouse Layer
Business Intelligence Introduction
Business Intelligence Tools
ETL - ELT Introduction
ETL
ELT
ETL vs ELT
Quiz

Data Modelling & ERD Notations

Data Modelling & Entity Relationship Diagram (ERD) Introduction
Data Modelling Overview
ERD Overview
Entity Attributes Relationships
Steps to Create an ERD
Build ERD using Chen's Notation Style
Build ERD using Information Engineering Notation Style
Data Modelling Concepts
Different Type of Keys
Recommended Tools for Creating ERD
Quiz

Normalisation & Denormalisation

What is Normalisation?
1st Normal Form
2nd Normal Form
3rd Normal Form
Pros & Cons of Normalised Model
What is De-Normalisation?
De-Normalisation Techniques
Pros & Cons of De-Normalised Model
Quiz

Data Warehouse Design Methodologies

Data Warehouse Design Methodologies Introduction
Inmon Methodology
Corporate Information Factory (CIF) Architecture Explained
Inmon Architecture
Pros & Cons of Inmon Methodology
Kimball Methodology
Processes of Kimball Methodology
Kimball Architecture
Pros & Cons of Kimball Methodology
Inmon vs Kimball
Hybrid Architecture
Data Vault Methodology Introduction
Data Vault Components
Data Vault Architecture & Example
Pros & Cons of Data Vault
Inmon vs Kimball vs Data Vault
One Big Table (OBT) / Wide Table
Pros & Cons of OBT
Data Modelling Then, Now & Next
Quiz

Dimensional Modelling

Dimensional Modelling Introduction
What is Dimensional Modelling?
Data Warehouse LifeCycle Overview
Program/Project Planning
Requirement Gathering
Concept & Steps of Dimensional Modelling
Select Business Process & Declare the Grain
Dimensions (Types)
Conformed Dimensions
Junk Dimensions
Degenerate Dimensions
Role Playing Dimensions
Slowly Changing Dimensions (SCD) - Intro
Type 0 - SCD (Slowly Changing Dimensions)
Type 1 - SCD (Slowly Changing Dimensions)
Type 2 - SCD (Slowly Changing Dimensions)
Type 3 - SCD (Slowly Changing Dimensions)
Type 4 - SCD (Slowly Changing Dimensions)
SCD - Store as Snapshots
Bridge Tables
Facts
Additive Facts
Semi-Additive Facts
Non-Additive Facts
Transaction Facts Tables
Periodic Facts Tables
Accumulative Facts Tables
Star Schema
Snowflake Schema
Quiz

(Hands-on) Building dimensional data warehouse

Introduction
Hands-on overview
Use-Case Introduction
Use-Case Detailed Discussion
Requirements Gathering
Data Profiling - Introduction
Data Profiling - Completed
AE Workbook - Walkthrough
Bus Matrix - High Level Entities
Conceptual Model
Architecture Design
Dimensional Modelling Introduction
Bus Matrix Detailed
Source to Target Mapping (Source to BQ Data Lake)
Source to Target Mapping (BQ Data Lake to Staging)
Dimensional Model (Attributes & Measures)
Source to Target Mapping (Data Lake to Data Warehouse)
Source to Target Mapping (Data Warehouse to OBT)
Logical Model Design
Physical Model Design
dbt overview
Physical Implementation (Staging Layer)
Physical Implementation (Staging Layer) Cont.
Physical Implementation Dim Tables (Data Warehouse Layer)
Physical Implementation Fact Tables (Data Warehouse Layer)
Physical Implementation (Analytics OBT)
Debugging (dbt)
Adding Tests (dbt)
Hands-on Complete

Setting up Environments

BigQuery Setup Introduction
BigQuery Tables Setup using CSV
BigQuery Tables Setup Using SQL Script
Git Repository Setup
dbt setup & Installation

Glossary

Glossary

Reviews

Karim
September 14, 2023
The instructor is focusing on everything except the actual work. The instructor will spend half of the video to tell us about what he will do in the video and not actually show what to do
Giannis
August 19, 2023
The course required a good level/knowledge around setting up the environment. That is where most of the time is being spent during the course. The overall material and intro to dbt was very good.
Allan
August 4, 2023
Yes, this is the level I was looking for. I fell into this field and I need to get the terminology down straight.
Shungu
July 23, 2023
This course is perfect for ushering you into the world of Analytics Engineering. As I was also advised, I would advise starting with the dbt fundamentals course (available for free on the dbt website) before jumping into this course. It makes things clearer and also makes it easier to follow along with Rahul in the hands-on labs. It will also help if you have some understanding of command-line interfaces and Git. Overall great course!
Vikas
July 7, 2023
Great course , Love the way he is teaching . He explain from requirement gathering to hand-on and talked about real time senarious.
Thyago
July 1, 2023
Great teacher. He knows what he's talking and give us tips on what is interesting to he aware about, for a greater learning experience in Database learning
John
May 31, 2023
He needs to not skip steps just because he already installed certain hings. I need the POV of a novice installing for first time. I hate command line!
UdemyStudy
May 24, 2023
Impressive course. As a novice data professional, this course seeks to expose me to all the concepts and terminology I’ve been hearing and trying to research while working on my current job.
Michaël
May 9, 2023
If you want to learn about data modeling, this is a good starting point because it covers the important topics. But this course could have used some extra reviewing cycles because I've encountered several errors, unclear explanations or discrepancies between slides and narration. So I have found it necessary to consult additional sources to get a good understanding of the topics covered. Detailed remarks: - Auto generated subtitles, so many mistakes. - The English grammar is not always correct, creating confusion sometimes as to what is exactly meant - Sometimes items in the slides are not explained or even mentioned by the voice-over - Some concepts explained in an unclear way, e.g. transitive dependency - Dimensional modeling > star schema > dim & fact mapping: incorrect labels. ProductName is an attribute, not a dimension. All the values below it are attribute values, not attributes. - Analytics engineering bootcamp excel file: misaligned data in the tables - It’s probably best to first take the dbt fundamentals course to have a better understanding of dbt. - In the hands-on part, more time could have been dedicated to explain the relation between the tables in the source database. The fact_purchase_order table is constructed by combining several of these tables (without much explanation), but I really have my doubts whether some of the joins between them logically make sense.
Avishek
April 5, 2023
This is a good course. A huge area is covered. I enjoyed the last handson project the most. 100% helpful.
Tomasz
January 30, 2023
Use join instead of left join + where is not null Some recordings started in the middle of the sentence. It could be better organized.
Yingtao
January 23, 2023
(1) A fine overview but there is very limited definition of technical terms.(the glossary table is not capturing most acronyms and technical terms mentioned throughout the course). (2) I had to downrate this course after getting very confused with the hands-on project setup. First, the ubuntu setup was poorly explained. It is not reasonable to simply share a link full of technical content without pointing students to the proper sections. While the setup went smoothly in the tutorial, errors popped up in the actual installation process, and the tutorial does not cover these issues. Second, the video jumps directly into VS Code without explaining how to get there from Ubuntu. I spent hours exploring this, and it was just a waste of time. A great course should make program setup a breeze so students can focus on the core and practice with the concepts. I would not recommend this course to anyone that hopes to get a technical taste of analytic engineering.
Waseem
January 15, 2023
I bought this course during my notice period prior to me starting a role as a Analytics Engineer, and this course really laid the foundations for me to understand data warehouse & data modelling. I learned way more than I had expected, and the course really out-delivered in this aspect. The format was excellent, I enjoyed the fact that it was taught via slideshows, as I was able to digest the information visually, as well as write notes whilst watching along (it was like I was back at University), it was formatted excellently, kudos to the instructor. The quiz's at the end of each section, also help to cement the concepts. I'm taking half a star away (I would have taken a whole star away had the other parts of this course not been so good), because the hands on dbt aspect of this course was rather thin, it was more of a overview, rather than a deep dive hands-on that I was expecting. If you're looking for a course to understand dbt, then this one ain't it- you'll be left wanting more. But if you want the theory and understanding needed to apply into dbt (such as data warehouse/data modelling, than this is the perfect course for that!). The instructor should really create a separate dbt bootcamp, as the hands on had a lot of potential and was delivered well (just was too thin/more of an overview than a deep dive).
Victor
January 3, 2023
You're pretty much on your own setting up the enviroment, not to considering the ones that he doesn't even mention and all of a sudden he is using them
Suresh
December 23, 2022
course content and coverage is good but the video slides are missing the written content information. so it's not easy to follow the course unless you have listening skills.

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4423012
udemy ID
11/30/2021
course created date
12/27/2021
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