Mastering Dbt (Data Build Tool) – From Beginner To Pro

Mastering Dbt (Data Build Tool) – From Beginner To Pro
Published 4/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

Analytics Engineering Bootcamp to take you from beginner to advanced dbt, and get hands-on with dbt Cloud!

What you’ll learn

How to build a complete dbt project from scratch

The main benefits of dbt, and a bit of background as to how it came about

All of the dbt fundamentals: sources, models, tests, documentation, snapshots, seeds, macros, hooks, and operations

How to structure a dbt project

How to version control changes to your code with GitHub

Advanced dbt testing – creating your own custom singular & generic tests, setting severity, and setting warn/error thresholds

Advanced dbt data modelling – including incremental and ephemeral models and what type to use (& when)

Advanced dbt commands – how to use different selectors, different profiles, tags, indirect test selection and building a local dbt documents site

How to use tests & macros from external packages to supercharge your dbt project

Best practises to use when running a dbt project (based on lots of experience!)

How to create a complete setup for Mac or Windows: installing all of the tools and getting a dbt specific VSCode setup!
Requirements

Basic SQL

No Python experience needed

Mac / Windows machine which is capable of installing Python, Git, and VSCode (we’ll run through all of this in the course!)
Description

Overview

Section 1: Introduction

Lecture 1 Instructor introduction

Lecture 2 Course outline

Lecture 3 Course introduction

Lecture 4 A brief history of the data stack

Lecture 5 Benefits of dbt – inferring dependencies

Lecture 6 Benefits of dbt – documentation & testing

Lecture 7 Benefits of dbt – Python-like functionality

Lecture 8 How dbt has solved a lot of problems in the data stack

Lecture 9 How dbt fits in the data stack

Lecture 10 dbt Core vs. dbt Cloud

Section 2: Getting set up with your tools

Lecture 11 Section overview

Lecture 12 Creating a Gmail account

Lecture 13 Setting up a BigQuery project with billing

Lecture 14 (Optional) If you have issues with BigQuery billing

Lecture 15 The BigQuery UI

Lecture 16 The dataset you’ll be using

Lecture 17 (Mac) Installing Python 3.10

Lecture 18 (Windows) Installing Python 3.10
Lecture 19 Downloading VSCode and setting up shortcuts

Lecture 20 Creating a GitHub account

Lecture 21 Forking vs. cloning

Lecture 22 Forking the repository

Lecture 23 (Optional) If you have issues syncing your forked repository

Lecture 24 Installing the recommended extensions in VSCode

Lecture 25 What’s a virtual environment (venv)?

Lecture 26 Setting up our virtual environment and installing packages

Lecture 27 Setting up dbt for BigQuery

Lecture 28 Trialling our model dbt project

Lecture 29 Setting up dbt autocomplete

Lecture 30 Run through of how our final project will look

Section 3: Building the basic dbt project

Lecture 31 The dbt init command

Lecture 32 Version control with GitHub

Lecture 33 Setting up dbt Power User

Lecture 34 How we’ll structure our project

Lecture 35 Creating our first source (src) yml file

Lecture 36 Creating our first staging (stg) SQL model

Lecture 37 Running our first staging (stg) SQL model

Lecture 38 Creating our first model yml file

Lecture 39 Adding tests to our first model yml file

Lecture 40 Setting up our models to materialise as tables instead of views

Lecture 41 Getting the rest of our staging (stg) SQL models set up

Lecture 42 Using dbt clean to get table materialisation working

Lecture 43 Getting the rest of the staging (stg) yml files set up

Lecture 44 Taking stock of our staging (stg) data models

Lecture 45 The target folder

Lecture 46 Getting our first intermediate (int) SQL model set up

Lecture 47 Getting our first intermediate (int) yml file set up

Lecture 50 Our basic dbt project is now complete!

Section 4: Advanced dbt: testing

Lecture 51 Setting default test severity in dbt_project.yml

Lecture 52 Setting test severity and thresholds

Lecture 53 The external dbt packages we’ll be using

Lecture 54 dbt_utils and dbt_expectations

Lecture 55 Custom singular tests

Lecture 56 Custom generic tests

Lecture 57 Applying advanced tests to our whole project

Lecture 58 Source freshness tests

Section 5: Advanced dbt: data modelling

Lecture 59 The doc function

Lecture 60 Seed files

Lecture 61 dbt snapshots

Lecture 62 Materialisation types

Lecture 63 Materialisation: ephemeral models

Lecture 64 Materialisation: incremental models

Section 6: Advanced dbt: commands and selectors

Lecture 66 Commands for a clean dbt run

Lecture 67 Using different dbt profiles

Lecture 68 Selectors

Lecture 69 Tags

Lecture 70 Indirect test selection

Lecture 71 dbt test with -warn-error

Lecture 72 dbt build

Lecture 73 dbt docs generate / serve

Section 7: Advanced dbt: jinja and macros

Lecture 74 Jinja comments, statements, and expressions

Lecture 75 The 3 types of macro: functions, hooks, operations

Lecture 76 (Optional) dbt Jinja function reference

Lecture 77 Macros: operations

Lecture 78 Macros: functions (building a basic macro)

Lecture 79 Macros: hooks

Lecture 81 (Optional) Jinja: using the target function

Section 8: dbt Cloud

Lecture 82 Creating a dbt Cloud account

Lecture 83 Setting up a service account

Lecture 84 Connecting GitHub to dbt Cloud

Lecture 85 The dbt Cloud IDE

Lecture 86 Deploying jobs on dbt Cloud

Data Analysts,Data Scientists,Analytics Engineers,Data Engineers,BI Professionals,Anyone interested in getting into data!