dbt (Data Build Tool): The Analytics Engineering Guide

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Wadson Guimatsa

4:30:55

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1 - Introduction and dbt setup

1 - Introduction.mp4

01:06

1 - Setting Up a Data Platform.txt

2 - GitHub repository.txt

2 - Resources and Guidelines for the Course.html

2 - store-sales-add-1k.csv

2 - tpcds-data-1gb.zip

3 - Create and Setup a Google Cloud Account.mp4

08:01

3 - create-big-query-tables.zip

3 - tpcds-data-1gb.zip

4 - Create Tables in Google BigQuery.mp4

05:20

5 - Create a dbt Cloud Account.mp4

05:28

6 - Create a GitHub Account.mp4

04:57

7 - About the Dataset.html

2 - Developing dbt models

8 - What is a dbt Models.mp4

01:57

9 - Creating Your First DBT Model.mp4

08:11

10 - Staging Models Fundamentals in dbt.mp4

02:12

11 - Intermediate Models Reading Assignment.html

12 - dbt Sources Introduction.mp4

02:47

13 - Creating and Configuring dbt Sources A StepbyStep Introduction.mp4

04:03

14 - dbt Sources How to Use the Source Function.mp4

05:29

15 - dbt Source Testing Essentials Ensuring Data Quality.mp4

10:39

16 - dbt Packages Leverage existing code for Efficient Analytics Workflows.mp4

04:55

17 - Utilizing dbt Packages Generating Sources and Staging Models.mp4

08:10

18 - dbt CodeGen Package Efficiently Generating Staging Models.mp4

08:12

19 - Documenting Your dbt Project How to Document Models and Sources.mp4

05:29

20 - Documenting Your dbt Models Best Practices and Tips.mp4

14:17

21 - ref function in dbt Introduction.mp4

01:56

22 - Understanding the ref function.mp4

06:59

23 - dbtcodegen Package Using the generatemodelyaml macro.mp4

04:53

24 - Collaborating with Your Team Using Pull Requests in GitHub.mp4

06:35

25 - dbt environments Introduction.mp4

02:16

26 - dbt Cloud Setting Up a Deployment Environment.mp4

03:27

27 - dbt Jobs Creating and Running dbt Jobs in Deployment Environments.mp4

06:58

28 - dbt Jobs Scheduling for Automated Execution.mp4

05:10

3 - dbt Core

29 - dbt Core Prerequisites Git Python and Google Cloud CLI.html

30 - dbt Core Installation.mp4

04:36

31 - dbt Core Initializing the GCloud CLI.mp4

03:19

32 - dbt Core Create Profiles Manually.mp4

04:22

33 - dbt Core dbt init Command Create Profiles and Project Automatically.html

34 - dbt Core Initial Local Run.mp4

02:13

35 - dbt Core Show Command CLI Only.mp4

04:08

36 - dbt Core Clean Command CLI Only.mp4

04:00

4 - Configuring dbt Project

37 - Introduction to project Configuration.mp4

06:01

38 - Project Configuration Part I.mp4

08:47

39 - Resource Configurations and Properties.mp4

04:55

40 - Model Configuration Config Block Table Materialization.mp4

04:19

41 - Resource Configuration Property File Table Materialization.mp4

05:39

42 - Resource Configuration DBT Project File Adding Tags.mp4

11:52

43 - Resource Configuration DBT Project File Using the Meta Configuration.mp4

10:29

44 - Incremental Models Introduction.mp4

01:10

45 - Incremental Models Setup.mp4

02:46

46 - Incremental Models Implementation Part I.mp4

07:51

47 - Incremental Models Implementation Part II.mp4

01:53

48 - Incremental Models Implementation Part III.mp4

05:05

49 - Incremental Models Implementation Part IV.mp4

05:09

50 - Ephemeral Models.mp4

10:05

5 - Analyses Seeds

51 - dbt Analyses.mp4

08:00

52 - dbt Seed Implementation.mp4

08:27

53 - dbt Seed Configuration.mp4

06:22

Description

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.

Who is this for?

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.

What You Need to Know?

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.

More details

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.Â The name â€œdbtâ€ 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.
Who this course is for:
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.

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Data Science

Wadson Guimatsa

Instructor's Courses

I'm a software developer specialized in building data-intensive applications.I've been developing software for over 10 years.I've worked for Industries that are very data-intensive such as the financials and industrial image processing.Over the years, the volume of data produced by systems and humans outgrew the storage and compute capacity of the legacy RDBMS systems, and therefore I had to learn how to use the new tools and frameworks to process Big-DataAs a data engineer, I'm very motivated and passionate about building applications that can leverage the power and flexibility of cloud computing and big-data processing frameworks.

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