Data Build Tool Dbt

Mastering Data Transformations with dbt: Build, Manage, and Optimize Scalable Data Workflows

What you'll learn

Learn what dbt is, its role in modern data workflows, and the concept of analytical engineering.

Create, initialize, and configure dbt projects for seamless data transformations.

Build robust dbt models, organize project structures, and use the ref function to manage dependencies.

Write, configure, and run generic and singular tests to ensure data quality and reliability.

Explore and implement dbt materializations, manage sources, and conduct freshness checks.

Use Jinja for creating custom macros to automate and streamline workflows.

Implement version control, set up monitoring and alerting, and schedule dbt runs for automated workflows.

Work with snapshots, hooks, incremental loads, and performance tuning to handle complex data challenges efficiently.

Requirements

Understanding of SQL queries, joins, and basic data manipulation is essential.

Knowledge of data warehouses like Snowflake, BigQuery, or Redshift is beneficial.

Basic understanding of how data is extracted, transformed, and loaded in workflows.

Familiarity with concepts like tables, schemas, and data types is helpful.

Knowing Python basics is advantageous, especially for custom scripts and advanced tasks.

Experience with Git or other version control systems is useful for collaboration.

Comfort with running basic commands in the terminal or command prompt is helpful.

A proactive attitude to learning new tools and solving data challenges.

Description

Master Data Transformation with dbt (Data Build Tool)This course is designed to equip you with the skills to build, transform, and manage modern data workflows using dbt (Data Build Tool). Learn how to implement analytical engineering principles, create robust data models, and ensure data quality through testing and validation. From setting up dbt projects to managing schema changes and optimizing performance, this course covers everything you need to become proficient in dbt.You’ll work hands-on with SQL, Jinja templates, and dbt macros, building reusable, scalable, and efficient data pipelines. By the end of this course, you’ll have the knowledge and practical experience to confidently use dbt for transforming raw data into actionable insights, collaborating on data projects, and automating workflows for any data warehouse environment.This course is perfect for data analysts, engineers, and anyone looking to enhance their data transformation skills with modern tools.By the end of this course, you’ll have the knowledge and practical experience to confidently use dbt for transforming raw data into actionable insights, collaborating on data projects, and automating workflows for any data warehouse environment. This course is perfect for data analysts, engineers, and anyone looking to enhance their data transformation skills with modern tools.

Overview

Section 1: Introduction to DBT

Lecture 1 What is DBT ?

Lecture 2 Create a DBT account

Lecture 3 Top Features of DBT

Lecture 4 Why use DBT? Exploring the Benefits for your Data Workflow

Lecture 5 What is Analytical Engineering?

Section 2: Account Setup

Lecture 6 Create a snowflake Account

Lecture 7 Explore the Snowflake Web UI interface

Lecture 8 Load sample data

Lecture 9 Setup the dbt project

Lecture 10 Initilize the dbt project

Lecture 11 Explore the DBT Cloud Web UI interface

Section 3: DBT Concepts

Lecture 12 Explore DBT Project Config file

Lecture 13 What are DBT models?

Lecture 14 Introduction to Creating a simple model

Lecture 15 Create test model in dbt

Lecture 16 Explore dbt model logs

Lecture 17 Build Your First dbt Model

Lecture 18 What is ref function in dbt

Lecture 19 Best Practices for Organizing Your dbt Project Structure

Lecture 20 Configuring Materializations in dbt

Lecture 21 Refactor your dim_customers model

Section 4: DBT Fundamentals

Lecture 22 What is dbt schema?

Lecture 23 What is macro?

Lecture 24 What is testing?

Lecture 25 What is dbt test?

Lecture 26 Different types of test in dbt

Lecture 27 What is generic test?

Lecture 28 Writing Generic Tests in dbt

Lecture 29 Writing singular Tests in dbt

Lecture 30 dbt Test Commands: Syntax and Usage

Section 5: Materializations

Lecture 31 What are materializations in DBT?

Lecture 32 Default Materializations in dbt

Lecture 33 Using config block for materializations

Section 6: Seeds and Sources

Lecture 34 What is sources in dbt?

Lecture 35 How to add sources in dbt?

Lecture 36 What is dbt source freshness?

Lecture 37 Implementing Source Freshness Checks in dbt

Lecture 38 What is dbt seed?

Lecture 39 Implementing dbt seeds in dbt

Section 7: DBT Cloud Features

Lecture 40 How to manage version control in dbt?

Lecture 41 How to set up Monitoring and Alerting in dbt?

Lecture 42 How to schedule DBT runs and automate data transformations?

Section 8: Jinja

Lecture 43 Introduction to Jinja

Section 9: DBT docs

Lecture 44 What is dbt docs?

Section 10: Advanced DBT Techniques

Lecture 45 Implementing table,view and ephemeral model

Lecture 46 Implementing incremental load in dbt

Lecture 47 Create Custom Macro

Lecture 48 What is dbt packages?

Section 11: Snapshots

Lecture 49 What are snapshots in DBT?

Lecture 50 Implementing snapshots in dbt

Section 12: Hooks

Lecture 51 What are hooks in DBT?

Lecture 52 Implementing hooks in DBT

Section 13: Analyses

Lecture 53 What is analyses?

Lecture 54 Implementing analyses in dbt

Section 14: Performance Optimization

Lecture 55 How to tuning dbt project?

Data Analysts: Looking to transition from manual data processes to scalable and automated workflows.,Data Engineers: Wanting to enhance their data pipeline efficiency and improve transformation processes.,Business Intelligence Professionals: Seeking tools to create robust data models and ensure data accuracy for reporting.,Data Scientists: Interested in building reusable data pipelines for analysis and machine learning projects.,ETL Developers: Exploring modern ELT approaches with dbt to replace or complement traditional ETL tools.,Database Administrators: Looking to manage and optimize data warehouse transformations and schema changes.,Tech Enthusiasts: Curious about modern data stack tools and eager to learn how to implement dbt in workflows.,Students and Beginners in Data: Starting their career in analytics or engineering and looking for hands-on experience with dbt.