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📕🔎 Building Modern Data Pipelines with dbt - Chapter 10: Data Tests

Chapter 10 explains how data quality can be systematically ensured using data tests.

This chapter covers:
- Understanding the purpose of data tests and why they matter in dbt • Executing tests using dbt test and dbt build
- Adding data tests to your dbt project
- Applying different generic tests in your dbt models
- Defining singular tests for custom test rules

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📕👀 Building Modern Data Pipelines with dbt - Chapter 8: Gold Layer

In Chapter 8 you learn how to implement the third layer of the Medallion Architecture: the gold layer, where data is transformed into business-ready dimensions and facts optimized for analytics and reporting.

This chapter covers:
- Configuring the gold layer in the dbt project
- Adding gold models and schemas to the dbt project
- Creating dimension and fact tables from silver data
- Applying business logic and aggregations
- Using views for analytics-ready datasets
- Documenting gold models with YAML files

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📕👀 Sneak Peek inside our book: Building Modern Data Pipelines with dbt

Chapter 7: Silver Layer

In Chapter 7 you learn how to implement the second layer of the Medallion Architecture: the silver layer, where raw data is cleaned, standardized, and prepared for reliable analytical use.

This chapter covers:
- Configuring the silver layer in the dbt project
- Adding silver models and schemas to the dbt project
- Using the source() function to reference raw data from the bronze layer
- Applying cleaning, casting, and standardization logic
- Building incremental and table-based silver models
- Documenting silver models with YAML files

👉 Find the book on Amazon - Link in Bio

📕👀 Sneak Peek inside our book: Building Modern Data Pipelines with dbt

Chapter 6: Bronze Layer

In Chapter 6 you begin implementing the first layer of the Medallion Architecture: the bronze layer, where raw data is ingested.

This chapter covers:
• Configuring the bronze layer in the dbt project
• Loading raw source data into DuckDB using dbt seeds
• Defining data sources with sources.yml
• Implementing source freshness checks to monitor data timeliness

👉 Find the book on Amazon - Link in Bio

📕👀 Sneak Peek inside our book: Building Modern Data Pipelines with dbt

Chapter 5: Getting Started

In Chapter 5 you’ll focus on setting up the practical foundation needed to start working with dbt — from creating a project to connecting it with DuckDB as a local analytics warehouse.

This chapter covers:

• Setting up a local Python development environment • Installing Python packages
• Creating and initializing a dbt project from scratch • Adding and managing dbt packages
• Configuring dbt to connect to DuckDB
• Validating the project and connection setup
• Setting up DuckDB UI

👉 Find the book on Amazon - Link in Bio

📕👀 Sneak Peek inside our book: Building Modern Data Pipelines with dbt

Chapter 4: Data Architecture

Chapter 4 explains the medallion architecture and the purpose of the bronze, silver, and gold layers.

This chapter covers:

• The purpose and principles of a modern data architecture
• The medallion architecture and its layered design
• The roles of the bronze, silver, and gold layers
• How data quality and business value evolve across layers
• Defining and visualizing dataflows from source to consumption

👉 Find the book on Amazon - Link in Bio

📕👀 Sneak Peek inside our book: Building Modern Data Pipelines with dbt

In Chapter 3, you are introduced to the fictional business use case and the raw dataset that serve as the foundation for the entire project. You will explore the structure and content of the source CSV files, gaining a clear understanding of the data.

This chapter covers:
• The fictional use case driving the analytics project
• The structure and purpose of the sample dataset
• The individual source files used as inputs for the dbt models

👉 Find the book on Amazon - Link in Bio

📕👀 Sneak Peek inside our book: Building Modern Data Pipelines with dbt

Chapter 2 covers the fundamentals of dbt and explains its position within modern ELT-based analytics workflows.

This chapter covers:

- What dbt is and why it’s used in modern analytics workflows
- The ELT approach and how dbt fits into it
- The differences between dbt Core and dbt Cloud
- dbt models and how they are materialized
- Available materialization strategies and when to use them
- Overview of all important YAML files for project configuration
- Using Jinja to add logic, reuse, and flexibility to SQL
- Applying the DRY principle in analytics engineering

👉 Find the book on Amazon - Link in Bio

📕👀 Sneak Peek inside our book: Building Modern Data Pipelines with dbt

Modern analytics starts with one thing: a powerful data platform.

Chapter 1 introduces data platform fundamentals and the role of data lakes, data warehouses, and data lakehouses in modern analytics.

This chapter covers:

- The role of a data platform in modern analytics
- An overview of common data platform technologies
- Key principles and characteristics of modern data storage architectures
- A comparison of data lakes, data warehouses, and data lakehouses, including their use cases
- An introduction to DuckDB as a lightweight, local analytical database

👉 Find the book on Amazon - Link in Bio

📕 Building Modern Data Pipelines with dbt: From Raw Data to Gold Standard with the Medallion Architecture

Our New Book is Out Now: Building Modern Data Pipelines with dbt Exciting News! Our new book has been released! 📕🚀 ➡️ Building Modern Data Pipelines with dbt: From Raw Data to Gold Standard with the Medallion Architecture by Fabian Hanik and Manuel Hanik VIEW ON AMAZON 📕 What is this book about? dbt has become one of the most important tools in the m...

🚀 New Book Out Now: Building Modern Data Pipelines with dbt 📕

Excited to share that our new book “Building Modern Data Pipelines with dbt: From Raw Data to Gold Standard with the Medallion Architecture” is officially out now! 📕

We wrote this book to provide a practical, hands-on guide to one of the most important tools in the modern data stack: dbt.

👉 Find the book on Amazon

This book will take you step by step through building data pipelines from scratch with dbt. You’ll follow a hands-on approach, supported by practical examples and clear visualizations that make complex concepts easy to understand.

🧠 Learn dbt from Scratch
Master the essentials of dbt - from setting up your first dbt project to building efficient data models. Explore advanced features like custom macros, data tests, data contracts, exposures, and managing access with grants.

💻 Hands-On Project
Work through a complete project using a fictional dataset. Implement each step yourself and gain practical, real-world dbt skills along the way.

🏅 Medallion Architecture
Learn how to structure your project with the Medallion Architecture for scalable, high-quality data pipelines. Process your data step by step, from its raw form in the Bronze layer, through the refined Silver layer, into a business-ready format in the Gold layer.

💬 Have a look and let us know what you think — we’d appreciate your feedback!

🎨🤖 A Complete Guide to Image Generation in Python with Hugging Face Diffusers

A Complete Guide to Image Generation in Python with Hugging Face Diffusers 📘 Introduction Generating images with AI has never been easier. Hugging Face’s Diffusers library provides a user-friendly way to create stunning visuals using pre-trained diffusion models like Stable Diffusion. In this guide, we’ll walk through the entire process step by step. 🧠 What are H...