Build Data Tool – AI Transformers

Design Your Own Data Solutions

Learn to create efficient data tools for business insights.

What you'll learn

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Course content

Introduction to DBT

a. Intro and benefits of DBT
b. DBT and Data Stack
c. DBT core and DBT cloud
d. ETL vs ELT
e. installation
f. Account setup
Project Setup and Structure

a. Set up with Snowflakes / Biquery (to be customized as per client)
b. Quering Data
c. Understanding the data to use for project <br. d. Understanding venv e. Quick demo of final project
Version control (Git) integration

a. GitHub Account
b. Forking Vs. Cloning
c. Forking the Repository
d. Setting up DBT Power User
e. Creating Our First Source (src) yml File
f. (Windows) Issues with the dbt Power User extension
g. Creating Our First Staging (stg) SQL Model
h. Running Our First Staging (stg) SQL Model
i. Creating Our First Model yml File
j. Adding Tests to Our First Model yml File
k. Setting Up Our Models to Materialize as Tables Instead of Views
l. Getting the Rest of Our Staging (stg) SQL Models Set Up
m. Using dbt clean to Get Table Materialization Working
n. Getting the Rest of the Staging (stg) yml Files Set Up
o. Taking Stock of Our Staging (stg) Data Models
p. The Target Folder
q. Getting Our First Intermediate (int) SQL Model Set Up
r. Getting Our First Intermediate (int) yml File Set Up
s. Getting Our Mart SQL Model Set Up
t. Getting Our Mart yml File Set Up
u. Ref Function
Understanding DBT directory structure

a. Directory Structure
b. Best Practices for Organizing Your DBT Project Structure
Profiles and environments

a. Using Different DBT Profiles
b. Intro Models
c. Model Config
d. DBT schema
e. CTE - Common Table Expressions
f. Creating our first model: Airbnb listings
g. DBT Power User - Working with Models, Autocomplete and Query Results (optional)
h. How to Manage Version Control in DBT?
i. How to Setup Monitoring and Alerting in DBT?
j. How to Schedule DBT Runs and Automate Data Transformations?
k. The doc Function
l. Seed Files
m. DBT Snapshots
n. (Optional) Partitioning a Table in BigQuery
o. Model Governance Overview
p. Model Governance - Access & Groups
q. Model Governance - Contracts
r. Model Governance - Versions
Running DBT Commands

a. Commands For a Clean DBT run
b. Selectors
c. Tags
d. Indirect Test Selection
e. DBT test With --warn-error
f. DBT build
g. DBT docs generate / serve
Introduction to DBT tests

a. DBT Schema
b. DBT Macro
c. Testing
d. DBT Test
e. Different Types of Test in DBT
f. What is Generic Test
g. Writing Generic Tests in DBT
h. Writing Singular Tests in DBT
i. DBT Test Commands: Syntax and Usage
Documentation and Data Lineage

a. Materialization Types
b. Materialization: Ephemeral Models
c. Materialization: Incremental Models
d. Materializations Overview
e. Model Dependencies and dbt's ref tag
f. Table type materialization & Project-level Materialization config
g. Incremental materialization
h. Ephemeral materialization
i. Seeds and Sources Overview
j. Seeds
k. Sources
l. Source Freshness
m. Documentation Overview
n. Writing and Exploring Basic Documentation
o. Markdown-based Docs, Custom Overview Page and Assets
p. The Linage Graph (Data Flow DAG)
q. DBT Power User - Lineage and Documentation (optional)
Building Documentation
Documenting models and columns

a. What is Analyses?
b. Implementing Analyses in DBT
c. Tuning DBT Project
Jinja and Macros

a. Jinja Comments, Statements, and Expressions
b. Snapshots
c. Sources
d. The 3 Types of Macro: Functions, Hooks, Operations
e. (Optional) dbt Jinja Function Reference
f. Macros: Operations
g. Macros: Functions (Building a Basic Macro)
h. Macros: Hooks
i. Jinja Statements: for Loops and Setting Variables
j. (Optional) Jinja: Using the Target Function
k. Introduction to Jinja
l. Implementing Table, View and Ephemeral Model
m. Implementing Incremental Load in DBT
n. Create Custom Macros
o. What is DBT Packages
DBT deployment on cloud
Best Practices for DBT Projects

a. Best practice and Certification guide
b. Environment
c. Styling with Common Table Expressions
d. Tags
e. Limiting Data
f. Continuous Integration with Github

Requirements

Description

The Build Data Tool course is designed for individuals and professionals who want to create custom data tools that streamline processes and deliver valuable insights. Through this course, participants will learn to design and develop data collection and organization tools that are efficient, secure, and user-friendly. Our training covers everything from data collection techniques and interface creation to AI integration for enhanced analysis. You’ll also explore methods to automate data workflows and visualize data in ways that make it easy to understand and act on. Participants will gain hands-on experience in building practical data tools tailored for various industries and business needs. With a focus on performance optimization and security, this course ensures that your tools are not only effective but also reliable and protected. By the end of the course, you will be equipped to design and implement data solutions that can transform business operations, improve decision-making, and provide a competitive edge. Whether you’re looking to improve internal processes or offer new solutions to clients, this course gives you the skills needed to build robust, impactful data tools.

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