Published 8/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.08 GB | Duration: 4h 33m
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.
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