Analytics Engineering Bootcamp

Name: Analytics Engineering Bootcamp
Rating: 4.3 (1957 reviews)

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

Created byRahul Prasad, David Badovinac

Last updated 5/2024

English

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

Course content

11 sections • 150 lectures • 12h 9m total length

Introduction8:28
Course Overview6:08
How to get the best out of this course3:13
Resources0:09
All resources required for this course.
Analytics Engineering Bootcamp Workbook - To follow source-to-target mapping
Cloud-SQL-OLTP-ERD - Entity Relationship Diagram to understand OLTP system
dl_northwind_csv - CSV files to populate BigQuery Data Lake layer
dl_northwind_csv - SQL scripts to populate BigQuery Data Lake layer (includes create tables script & insert data scripts)
entity-relationship-diagram - Designing Conceptual, Logical & Physical models using draw.io
GitHub Repository (Containing all the dbt models) - https://github.com/RahulPrasad/analytics-engineer-bootcamp

Data Warehouse Design Methodologies Introduction1:14
Inmon Methodology4:52
Corporate Information Factory (CIF) Architecture Explained9:19
Inmon Architecture2:15
Pros & Cons of Inmon Methodology2:11
Kimball Methodology1:58
Processes of Kimball Methodology2:34
Kimball Architecture2:30
Pros & Cons of Kimball Methodology2:47
Inmon vs Kimball1:41
Hybrid Architecture2:34
Data Vault Methodology Introduction2:53
Data Vault Components3:18
Data Vault Architecture & Example4:47
Pros & Cons of Data Vault2:19
Inmon vs Kimball vs Data Vault3:03
One Big Table (OBT) / Wide Table4:16
Pros & Cons of OBT2:28
Data Modelling Then, Now & Next5:39
Quiz

Dimensional Modelling Introduction1:39
What is Dimensional Modelling?1:58
Data Warehouse LifeCycle Overview4:49
Program/Project Planning9:52
Requirement Gathering11:02
Concept & Steps of Dimensional Modelling6:35
Select Business Process & Declare the Grain6:20
Dimensions (Types)2:15
Conformed Dimensions2:57
Junk Dimensions2:58
Degenerate Dimensions3:45
Role Playing Dimensions1:38
Slowly Changing Dimensions (SCD) - Intro1:46
Type 0 - SCD (Slowly Changing Dimensions)0:52
Type 1 - SCD (Slowly Changing Dimensions)0:41
Type 2 - SCD (Slowly Changing Dimensions)2:43
Type 3 - SCD (Slowly Changing Dimensions)1:14
Type 4 - SCD (Slowly Changing Dimensions)1:43
SCD - Store as Snapshots2:40
Bridge Tables1:29
Facts1:57
Additive Facts1:58
Semi-Additive Facts1:48
Non-Additive Facts0:56
Transaction Facts Tables1:58
Periodic Facts Tables2:17
Accumulative Facts Tables2:05
Star Schema3:57
Snowflake Schema3:29
Quiz

Introduction0:25
Hands-on overview0:49
Use-Case Introduction1:09
Use-Case Detailed Discussion3:36
Requirements Gathering2:16
Data Profiling - Introduction14:31
Data Profiling - Completed2:47
AE Workbook - Walkthrough2:50
Bus Matrix - High Level Entities11:31
Conceptual Model8:40
Architecture Design3:12
Dimensional Modelling Introduction2:16
Bus Matrix Detailed9:35
Source to Target Mapping (Source to BQ Data Lake)12:21
Source to Target Mapping (BQ Data Lake to Staging)4:14
Dimensional Model (Attributes & Measures)9:28
Source to Target Mapping (Data Lake to Data Warehouse)10:23
Source to Target Mapping (Data Warehouse to OBT)2:41
Logical Model Design8:06
Physical Model Design14:39
dbt overview8:33
Physical Implementation (Staging Layer)20:46
Physical Implementation (Staging Layer) Cont.5:14
Physical Implementation Dim Tables (Data Warehouse Layer)26:27
Physical Implementation Fact Tables (Data Warehouse Layer)13:51
Physical Implementation (Analytics OBT)15:50
Debugging (dbt)2:09
Adding Tests (dbt)2:12
Hands-on Complete0:41

Introduction to dbt Power User1:28
dbt Power User pre-requisites0:07
Installation and Configuation10:07
Generate dbt Models from source file or SQL4:39
Query Translation3:28
Query Explanation3:56
dbt Actions tool and Query results preview6:00
Update dbt Model4:07
Project Governance8:04
Write dbt Tests Automatically6:07
In this section, packages.yml (https://docs.getdbt.com/docs/build/packages) file is required to be generated with below contents. This should be at the same level as your dbt_project.yml file

packages:
- package: calogica/dbt_expectations
version: 0.10.3
- package: dbt-labs/dbt_utils
version: 1.1.1
Impact Analysis with Column Lineage4:42
Documentation Generation4:44
Collaboration Workflow5:54
Defer to prod5:09

Requirements

No prior experience is required, however, if you have some basic understanding of SQL it'll help you understand this course better

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

Analytics Engineering Bootcamp

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 18min

What is a database?17 lectures • 57min

What is a data warehouse?13 lectures • 1hr 23min

Data Modelling & ERD Notations10 lectures • 56min

Normalisation & Denormalisation8 lectures • 30min

Data Warehouse Design Methodologies19 lectures • 1hr 3min

Dimensional Modelling29 lectures • 1hr 29min

Setting up Environments6 lectures • 39min

(Hands-on dbt) Building dimensional data warehouse29 lectures • 3hr 41min

Accelerate dbt Development with Power User for dbt (DataPilot)14 lectures • 1hr 9min

Requirements

Description

Who this course is for: