The Complete Dbt (Data Build Tool) Bootcamp: Zero To Hero

The Complete Dbt (Data Build Tool) Bootcamp: Zero To Hero
Last updated 11/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

Learn Analytics Engineering with this dbt™ course covering theory & practice through a real-world Airbnb use case.

What you’ll learn

Learn to use the dbt™ platform professionally through the creation of an exhaustive, real-world, hands-on dbt – Airbnb project covering both Theory and Practice

Set up the complete development environment on Mac & Windows, Connect to Snowflake and BI, Configure dbt profile, extend the IDE with dbt tools
Learn core dbt concepts such as Models, Materialization, Sources, Seeds, Snapshots, Packages, Hooks, Exposures, Analyses, write complex SQL queries

Implement singular and generic dbt tests, work with additional arguments and default config values, customize dbt built-in tests

Document your models and pipeline, customize the dbt docs page, Explore and analyse dependencies between transformation steps

Understand how dbt fits into the modern data stack, learn about the stages of the Data-Maturity Model, and well functioning Data Architectures

Master ETL/ELT procedures, Data Transformations, Modern Data Stack, Slowly Changing Dimensions, Common Table Expressions and Analytics Engineering

Understand what is a Data Warehouse, Data Lake, or Data Lakehouse and when to use which, handle Data Collection, Data Wrangling and Data Integrations

See how advanced testing works using dbt-expectations, a Great Expectations inspired testing framework

Requirements

Basic SQL experience

No previous programming language experience required

Working computer (Mac/Windows/Linux)
Network access whitelist to snowflake(.com) and GitHub if you work behind a firewall or VPN

Git and Python (We are linking to the installation instructions of these tools in the course)

Description

Overview

Section 1: Course Introduction

Lecture 1 Instructors Introduction

Lecture 2 Welcome

Lecture 3 Course Structure Overview

Section 2: Theory – The Data Maturity Model

Lecture 4 Introduction – Maslow’s Pyramid of Data

Lecture 5 The Data Maturity Model

Lecture 6 ETL and ELT

Section 3: Theory – Data Warehouses, Data Lakes and Lakehouses

Lecture 7 Data Warehousing – a short introduction

Lecture 8 External Tables and Cloud Data Warehouses

Lecture 9 Data Lakes

Lecture 10 Data Lakehouse

Section 4: Theory – The Modern Data Stack

Lecture 11 The Modern Data Stack

Section 5: Theory – Slowly Changing Dimension (SCD)

Lecture 12 The Basics of Slowly Changing Dimensions

Lecture 13 Type 0 – Retain Original

Lecture 14 Type 1 – Overwrite

Lecture 15 Type 2 – Add New Row

Lecture 16 Type 3 – Add New Attribute

Section 6: Intro to the practical sessions: dbt and the Airbnb use-case

Lecture 17 dbt Overview

Lecture 18 Use-case and Input Data Model Overview

Section 7: Practice – Setup

Lecture 19 How to use github and the course’s resources

Lecture 20 Snowflake Registration

Lecture 21 A note on the Snowflake data import

Lecture 22 Importing Airbnb Data into Snowflake

Lecture 23 READ ME! Setup instructions and Prerequisites

Lecture 24 dbt setup – Mac

Lecture 25 Optional – Installing Python and pip on Windows
Lecture 26 Optional – Setting up a Python Virtualenv on Windows
Lecture 27 dbt setup – Windows
Lecture 28 Creating a dbt project and connecting it to Snowflake

Lecture 29 Install dbt power tools into VSCode Setup (optional)

Lecture 30 READ ME – dbt project structure – data folder vs. seeds folder

Lecture 31 Overview of the dbt Project Structure

Lecture 32 A note on the DEV schema

Lecture 33 Datasets and Data Flow Overview

Section 8: Models

Lecture 34 Learning Objectives – Models

Lecture 35 Models Overview

Lecture 36 Theory: CTE – Common Table Expressions

Lecture 37 Creating our first model: Airbnb listings

Section 9: Materializations

Lecture 38 Learning Objectives – Materializations

Lecture 39 Materializations Overview

Lecture 40 Model Dependencies and dbt’s ref tag

Lecture 41 Table type materialization & Project-level Materialization config

Lecture 42 Incremental materialization

Lecture 43 Ephemeral materialization

Section 10: Seeds and Sources

Lecture 44 Learning Objectives – Seeds and Sources

Lecture 45 Seeds and Sources Overview

Lecture 46 Seeds

Lecture 47 Sources

Lecture 48 Source Freshness

Section 11: Snapshots

Lecture 49 Learning Objectives – Snapshots

Lecture 50 Snapshots Overview

Lecture 51 Creating a Snapshot

Section 12: Tests

Lecture 52 Learning objectives – Tests

Lecture 53 Tests Overview

Lecture 54 Generic Tests

Lecture 55 Singular Tests

Section 13: Macros, Custom Tests and Packages

Lecture 56 Learning Objectives – Macros, Custom Tests and Packages

Lecture 57 Macros Overview

Lecture 58 Creating our First Macro

Lecture 59 Writing Custom Generic Tests

Section 14: Documentation

Lecture 61 Learning Objectives – Documentation

Lecture 62 Documentation Overview

Lecture 63 Writing and Exploring Basic Documentation

Lecture 64 Markdown-based Docs, Custom Overview Page and Assets

Lecture 65 The Linage Graph (Data Flow DAG)

Section 15: Analyses, Hooks and Exposures

Lecture 66 Learning Objectives – Analyses, Hook and Exposures

Lecture 67 Analyses

Lecture 68 Hooks

Lecture 69 Setting up a BI Dashboard in Snowflake and Preset

Lecture 70 Exposures

Section 16: dbt Hero

Lecture 71 Welcome to Hero

Lecture 72 Have your say in the course’s roadmap

Section 17: Debugging Tests and Testing with dbt-expectations

Lecture 73 A note on the dbt-expectations setup

Lecture 74 Great Expectations Overview

Lecture 75 Comparing row counts between models

Lecture 76 Looking for outliers in your data

Lecture 77 Implementing test warnings for extremal items

Lecture 78 Validating column types

Lecture 80 Debugging dbt tests and Working with regular expressions

Section 18: Supplementary Materials

Lecture 81 Supplementary Material – Installing dbt on Windows with Windows Linux Filesystem
Analytics Engineers,Data Analysts,BI Analysts,Data Scientists,Data Engineers