dbt (Data Build Tool): The Analytics Engineering Guide

dbt (Data Build Tool): The Analytics Engineering Guide, available at $19.99, has an average rating of 5, with 66 lectures, based on 2 reviews, and has 47 subscribers.

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

Enroll now: dbt (Data Build Tool): The Analytics Engineering Guide

Summary

Title: dbt (Data Build Tool): The Analytics Engineering Guide

Price: $19.99

Average Rating: 5

Number of Lectures: 66

Number of Published Lectures: 66

Number of Curriculum Items: 66

Number of Published Curriculum Objects: 66

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

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.

Target Audiences

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.

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.
Testing Data Models:Learn how to use built-in tests in dbt and create custom ones.

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.

Course Curriculum

Chapter 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

Chapter 2: Developing dbt models

Lecture 1: What is a dbt Models

Lecture 2: Creating Your First DBT Model

Lecture 3: Staging Models Fundamentals in dbt

Lecture 4: Intermediate Models: Reading Assignment

Lecture 5: dbt Sources: Introduction

Lecture 6: Creating and Configuring dbt Sources: A Step-by-Step Introduction

Lecture 7: dbt Sources: How to Use the Source Function

Lecture 8: dbt Source Testing Essentials: Ensuring Data Quality

Lecture 9: dbt Packages: Leverage existing code for Efficient Analytics Workflows

Lecture 10: Utilizing dbt Packages: Generating Sources and Staging Models

Lecture 11: dbt Code-Gen Package: Efficiently Generating Staging Models

Lecture 12: Documenting Your dbt Project: How to Document Models and Sources

Lecture 13: Documenting Your dbt Models: Best Practices and Tips

Lecture 14: ref function in dbt: Introduction

Lecture 15: Understanding the ref function

Lecture 16: dbt-codegen Package: Using the generate_model_yaml macro

Lecture 17: Collaborating with Your Team Using Pull Requests in GitHub

Lecture 18: dbt environments: Introduction

Lecture 19: dbt Cloud: Setting Up a Deployment Environment

Lecture 20: dbt Jobs: Creating and Running dbt Jobs in Deployment Environments

Lecture 21: dbt Jobs: Scheduling for Automated Execution

Chapter 3: dbt Core

Lecture 1: dbt Core Prerequisites: Git, Python and Google Cloud CLI

Lecture 2: dbt Core: Installation

Lecture 3: dbt Core: Initializing the GCloud CLI

Lecture 4: dbt Core: Create Profiles Manually

Lecture 5: dbt Core: dbt init Command – Create Profiles and Project Automatically

Lecture 6: dbt Core – Initial Local Run

Lecture 7: dbt Core: Show Command – CLI Only

Lecture 8: dbt Core: Clean Command – CLI Only

Chapter 4: Configuring dbt Project

Lecture 1: Introduction to project Configuration

Lecture 2: Project Configuration Part I

Lecture 3: Resource Configurations and Properties

Lecture 4: Model Configuration: Config Block – Table Materialization

Lecture 5: Resource Configuration: Property File – Table Materialization

Lecture 6: Resource Configuration: DBT Project File – Adding Tags

Lecture 7: Resource Configuration: DBT Project File – Using the Meta Configuration

Lecture 8: Incremental Models – Introduction

Lecture 9: Incremental Models – Setup

Lecture 10: Incremental Models – Implementation Part I

Lecture 11: Incremental Models – Implementation Part II

Lecture 12: Incremental Models – Implementation Part III

Lecture 13: Incremental Models – Implementation Part IV

Lecture 14: Ephemeral Models

Chapter 5: Analyses & Seeds

Lecture 1: dbt Analyses

Lecture 2: dbt Seed: Implementation

Lecture 3: dbt Seed: Configuration

Chapter 6: Node Selection Syntax

Lecture 1: dbt's Node Selection Syntax – Introduction

Lecture 2: The Select Argument

Lecture 3: Graph Operators

Lecture 4: The Exclude Argument

Lecture 5: dbt List Command

Lecture 6: Selector Methods

Chapter 7: dbt Testing: How to test your dbt resources

Lecture 1: Introduction to Testing in dbt

Lecture 2: dbt test: Referential Integrity Test

Lecture 3: Custom Generic Test

Lecture 4: Testing Using Packages: How to Use the dbt Utils Package

Lecture 5: Testing Using Packages: How to Use the dbt Expectations Package

Lecture 6: dbt Singular Test

Lecture 7: Configuring dbt Test

Instructors

Wadson Guimatsa
Data Engineer

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