Dbt (Data Build Tool): The Analytics Engineering Guide

Elevate Your Analytics Workflows: Transform data with dbt Cloud & dbt Core and Apply Software Engineering best practices

What you'll learn

Managing dbt Projects: Learn to initiate, structure, and effectively manage dbt projects, including dbt profiles understanding.

Master dbt Models: Understand how to create and manage dbt models, including their dependencies, configurations.

Grasp dbt's Core Purpose: You will confidently articulate what dbt is and its crucial role in data engineering.

Implement Testing in dbt: Understand the different types of tests in dbt, and how to implement them effectively for different models and other dbt resources..

Understand dbt Packages: Gain knowledge on how to use dbt packages to modularize and reuse code across different dbt projects.

Deploy dbt Cloud Jobs: Learn how to configure and deploy dbt jobs in various environments, understanding the differences and requirements of each.

Create and Maintain dbt Documentation: Learn how to generate and maintain documentation within dbt, including descriptions of sources, tables, and columns.

Setting Up and Installing dbt: you should be able to navigate the process of installing dbt and setting it up whether that's a local machine or dbt cloud

Version Control: Understand how dbt integrates with platforms like GitHub to provide version control, ensuring you can track and manage changes effectively.

Streamlined Workflows: Instead of juggling multiple tools and platforms, learn how dbt serves as a one-stop solution for most of your data transformation needs.

dbt Cloud IDE: Master how to use dbt Cloud IDE to write, test, and deploy DBT models and other resources without needing to interact with the command line.

Requirements

Foundational SQL Knowledge: While the course will delve into dbt, which builds upon SQL, students should be comfortable with basic SQL queries, joins, and aggregations

Hands-On Approach: An inclination to apply knowledge practically will be beneficial.

Willingness to Learn and Install Software: While the course will guide through the essentials, students should be open to installing and exploring new software and tools as required.

Description

Take your skills as a data professional to the next level with this Hands-on Course course on dbt, the Data Build Tool.Start your journey toward mastering Analytics Engineering by signing up for this course now!This course aims to give you the necessary knowledge and abilities to effectively use dbt in your data projects and help you achieve your goals.This course will guide you through the following:Understanding the dbt architecture: Learn the fundamental principles and concepts underlying dbt.Developing dbt models: Discover how to convert business logic into performant SQL queries and create a logical flow of models.Debugging data modeling errors: Acquire skills to troubleshoot and resolve errors that may arise during data modeling.Monitoring data pipelines: Learn to monitor and manage dbt workflows efficiently.Implementing dbt tests: Gain proficiency in implementing various tests in dbt to ensure data accuracy and reliability.Deploying dbt jobs: Understand how to set up and manage dbt jobs in different environments.Creating and maintaining dbt documentation: Learn to create detailed and helpful documentation for your dbt projects.Promoting code through version control: Understand how to use Git for version control in dbt projects.Establishing environments in data warehouses for dbt: Learn to set up and manage different environments in your data warehouse for dbt projects.By the end of this course, you will have a solid understanding of dbt, be proficient in its use, and be well-prepared to take the dbt Analytics Engineering Certification Exam. Whether you're a data engineer, a data analyst, or anyone interested in managing data workflows, this course will provide valuable insights and practical knowledge to advance your career.Please note that this course does not require any prior experience with dbt. However, familiarity with SQL and basic data engineering concepts will be helpful.Disclaimer: This course is not affiliated, associated, authorized, endorsed by, or in any way officially connected with dbt Labs, Inc. or any of its subsidiaries or its affiliates.  The name “dbt” and related names, marks, emblems, and images are registered trademarks of dbt Labs, Inc. Similarly; this course is not officially connected with any data platform or tools mentioned in the course. The course content is based on the instructor's experience and knowledge and is provided only for educational purposes.

Overview

Section 1: Introduction and dbt setup

Lecture 1 Introduction

Lecture 2 Resources and Guidelines for the Course

Lecture 3 Create and Setup a Google Cloud Account

Lecture 4 Create Tables in Google BigQuery

Lecture 5 Create a dbt Cloud Account

Lecture 6 Create a GitHub Account

Lecture 7 About the Dataset

Section 2: Developing dbt models

Lecture 8 What is a dbt Models

Lecture 9 Creating Your First DBT Model

Lecture 10 Staging Models Fundamentals in dbt

Lecture 11 Intermediate Models: Reading Assignment

Lecture 12 dbt Sources: Introduction

Lecture 13 Creating and Configuring dbt Sources: A Step-by-Step Introduction

Lecture 14 dbt Sources: How to Use the Source Function

Lecture 15 dbt Source Testing Essentials: Ensuring Data Quality

Lecture 16 dbt Packages: Leverage existing code for Efficient Analytics Workflows

Lecture 17 Utilizing dbt Packages: Generating Sources and Staging Models

Lecture 18 dbt Code-Gen Package: Efficiently Generating Staging Models

Lecture 19 Documenting Your dbt Project: How to Document Models and Sources

Lecture 20 Documenting Your dbt Models: Best Practices and Tips

Lecture 21 ref function in dbt: Introduction

Lecture 22 Understanding the ref function

Lecture 23 dbt-codegen Package: Using the generate_model_yaml macro

Lecture 24 Collaborating with Your Team Using Pull Requests in GitHub

Lecture 25 dbt environments: Introduction

Lecture 26 dbt Cloud: Setting Up a Deployment Environment

Lecture 27 dbt Jobs: Creating and Running dbt Jobs in Deployment Environments

Lecture 28 dbt Jobs: Scheduling for Automated Execution

Section 3: dbt Core

Lecture 29 dbt Core Prerequisites: Git, Python and Google Cloud CLI

Lecture 30 dbt Core: Installation

Lecture 31 dbt Core: Initializing the GCloud CLI

Lecture 32 dbt Core: Create Profiles Manually

Lecture 33 dbt Core: dbt init Command - Create Profiles and Project Automatically

Lecture 34 dbt Core - Initial Local Run

Lecture 35 dbt Core: Show Command - CLI Only

Lecture 36 dbt Core: Clean Command - CLI Only

Section 4: Configuring dbt Project

Lecture 37 Introduction to project Configuration

Lecture 38 Project Configuration Part I

Lecture 39 Resource Configurations and Properties

Lecture 40 Model Configuration: Config Block - Table Materialization

Lecture 41 Resource Configuration: Property File - Table Materialization

Lecture 42 Resource Configuration: DBT Project File - Adding Tags

Lecture 43 Resource Configuration: DBT Project File - Using the Meta Configuration

Lecture 44 Incremental Models - Introduction

Lecture 45 Incremental Models - Setup

Lecture 46 Incremental Models - Implementation Part I

Lecture 47 Incremental Models - Implementation Part II

Lecture 48 Incremental Models - Implementation Part III

Lecture 49 Incremental Models - Implementation Part IV

Lecture 50 Ephemeral Models

Section 5: Analyses & Seeds

Lecture 51 dbt Analyses

Lecture 52 dbt Seed: Implementation

Lecture 53 dbt Seed: Configuration

Beginners in data analytics who are starting their journey with data processing tools and are looking for a thorough understanding of dbt.,SQL practitioners of all levels looking to comprehensively incorporate dbt into their data processing toolset.,Business analysts who work with data regularly and aim to optimize their workflow with a more in-depth understanding of dbt.,Data engineers and data scientists enthusiastic about harnessing dbt's complete capabilities for improved ETL/ELT workflows, testing, and analytics.,Professionals transitioning into data roles and seeking a hands-on introduction to a popular data build tool.