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
Advanced dbt jinja & macros - creating your own macros to use in hooks / functions / operations, using jinja for loops and variables, and the target function
How to deploy your project on dbt Cloud, how to use the dbt Cloud UI, and using environment variables
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
Welcome to the 2023 Mastering dbt (data build tool) course!Thank you for taking the time to look at my course! I have over 7 years of experience across Analytics / Analytics Engineering / Data Science, including 4 years using dbt on a daily basis. I was also involved in the rollout of dbt in my time at Monzo.MY APPROACH TO THIS COURSE:This course is designed to take you from only needing beginner SQL to being comfortable with everything in dbt: from the basic data modelling right through to all of the advanced features such as creating custom tests and macros. We'll be doing this step by step, and build from the basics upwards.It's focused on practical outcomes - we won't be spending ages on database theory, or going into lots of detail on the dataset, instead we'll be aiming to get you up to advanced dbt levels as quickly as possible.For this course, we'll be using a free public dataset in Google BigQuery centred around a fictional eCommerce business.Use Snowflake, Redshift, Databricks, or anything other than BigQuery? Don't worry! I've used dbt with both BigQuery and Snowflake myself and all of the knowledge you learn on this course will be transferable.The focus of this course is not around exploring the entire dataset - instead I've tried to keep it to the minimum number of data models needed to get the core concepts across.Access to this dataset is free, and the amount of data you'll be using in BigQuery will be free. I go into this a bit more in the course - but please don't worry about any additional costs.For every video where we're writing code, I've created lesson attachments with the final outputs. This means you can either code as you go along, or watch the videos and look at the handouts afterwards! I've also included some theory with these handouts to help hammer home the points made in the videos.This course isn't static! I'd love to hear your feedback and will be updating this course on an ongoing basis.COURSE STRUCTURE:This course focuses on first getting a good understanding of what problems dbt solves, then building a basic dbt project, before layering on more advanced concepts and finally deploying our project with dbt Cloud.IntroductionA bit of theory (<1 hour) around dbt, what problems existed in the data stack before it came along, and how it solves them.Tool setupGetting set up with Python, GitHub, Google BigQuery, VSCode, and of course dbt! If you're familiar with any of these tools already then you are more than welcome to skip the appropriate lessons.We'll also be exploring the fictional eCommerce dataset that we'll be using throughout the course.Building our basic dbt projectThis section focuses on creating our project from scratch, including how we will structure our project. We'll be building out staging (stg), intermediate (int), and mart data models, including documentation & testing with the out-of-the-box dbt tests.Advanced dbt testingWe'll start to build on our basic dbt project by setting test severity & thresholds, using the dbt-utils and dbt-expectations external packages for their excellent selection of tests, creating our own custom singular & generic tests, and testing the freshness of our source data.Advanced data modelling with dbtNext, we'll be looking at how we can create reusable documentation, seed files (version controlled .csv files), snapshots (capturing changes to data tables), and materialisation methods.Most of this section will be focused on the last part - the materialisation methods: ephemeral, view, table, and incremental. By this point we'll have encountered view & table models and we will be building both an incremental and an ephemeral model - and you will gain an understanding of what to use and when.Advanced dbt commandsThis section will focus less on changing our dbt project, but instead all of the major dbt commands and how (and when) to use them.Advanced Jinja & macrosThe final changes to our project will involve using Jinja - a core feature of dbt and arguably it's most complex but powerful feature - and using it to create our own macros.This section will run through how you can use Jinja macros for hooks, operations, and as reusable functions in your SQL models. It'll also run through some theory around Jinja, common mistakes, and what I (personally) find to be what it's most useful for!dbt CloudFinally, we'll be exploring how to take our project and deploy it on dbt Cloud - including how to schedule it to run on a regular basis. We'll also be looking at dbt Cloud itself and its main benefits.

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 48 Getting our mart SQL model set up

Lecture 49 Getting our mart 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

Lecture 65 (Optional) Partitioning a table in BigQuery

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 80 Jinja statements: for loops and setting variables

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!

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