Build an End-to-End DBT + Snowflake Pipeline

Introduction

In today’s rapidly evolving data engineering landscape, mastering tools like DBT, Snowflake, and AWS is critical for professionals aiming to handle complex data pipelines. This article provides a transformative learning experience by walking through the creation of an end-to-end data pipeline using DBT (Data Build Tool), Snowflake, and AWS. Combining real-world insights with advanced techniques, this guide focuses on creating a portfolio-worthy project that emphasizes incremental data loading, metadata-driven pipelines, star schemas, and industry-aligned practices.

By following this tutorial, you will not only gain hands-on experience with DBT and Snowflake but also understand advanced concepts like creating metadata-driven pipelines, incremental data loads, and building slowly changing dimensions (SCD) Type 2. Whether you’re an aspiring data engineer or a mid-level professional looking to level up, this guide is your one-stop destination for building scalable and industry-relevant pipelines.

sbb-itb-61a6e59

What You’ll Learn

By the end of this guide, you’ll be able to:

• Set up DBT, Snowflake, and AWS environments.
• Build a medallion architecture pipeline (bronze, silver, gold layers).
• Use incremental data loading to optimize data ingestion.
• Create metadata-driven pipelines for dynamic scalability.
• Understand and implement Ginga templates in DBT.
• Build a star schema with fact and dimension tables, including SCD Type 2.
• Run data quality tests to ensure reliability.

Let’s dive in!

Step 1: Setting Up the Environment

Before building your pipeline, you need to set up the foundational tools and platforms: AWS S3, Snowflake, and DBT.

Getting Started with AWS S3 and Snowflake

1. Create an AWS Account: AWS provides free-tier access for new users. Use this to create an S3 bucket for storing source data.
2. Snowflake Setup: Sign up for Snowflake, and take advantage of the free trial with $400 in credits. Select AWS as your cloud provider for seamless integration.

Installing DBT and Tools

1. Install Python (Version 3.12): Ensure compatibility with DBT by installing this specific version.
2. Install DBT Core: Use pip install dbt-core to set up DBT locally.
3. Install DBT Adapters (e.g., Snowflake): DBT requires an adapter, such as pip install dbt-snowflake, to interact with your data warehouse.
4. Choose an IDE: Use a developer-friendly code editor like VS Code or Anti-Gravity.

After setting up these environments, you’re ready to start building layers in your data pipeline.

Step 2: Constructing the Medallion Architecture

The medallion architecture consists of Bronze, Silver, and Gold layers, each serving distinct purposes in the pipeline:

Bronze Layer: Ingesting Raw Data

• The Bronze layer stores raw, untransformed data from the source (AWS S3).
• Use Snowflake to create staging tables and manage permissions via IAM roles.
• Implement incremental loading to optimize data ingestion. This means loading only new or changed data.

SQL Example for Incremental Loading in DBT:

select *
from {{ source('staging', 'bookings') }}
where created_at > (
    select max(created_at)
    from {{ this }}
    )

Key Concepts:

• IAM Roles: Secure your data by integrating AWS credentials with Snowflake stages.
• DBT Materialization: Automate the creation of tables and views using DBT configurations.

Silver Layer: Transforming Data

This layer applies business logic and transformations. For instance:

• Use Ginga templates to dynamically create reusable SQL transformations.
• Transform raw data into cleaned, enriched datasets.

Example of Using a Ginga Template for Parameterization:

select *
from {{ ref('bronze_bookings') }}
where {{ filter_column }} > {{ threshold }}

Key Concepts:

• Dynamic Transformations: Write modular transformations that adapt with parameterized inputs.
• Error Handling: Follow industry best practices for clean, validated transformations.

Gold Layer: Building Analytical Models

The Gold layer serves as the final presentation layer for analytics:

1. One Big Table (OBT): Consolidate all tables into one comprehensive dataset.
2. Star Schema: Break the OBT into fact and dimension tables for analytical efficiency.

Metadata-Driven Pipelines: Metadata-driven pipelines dynamically determine joins and configurations. Instead of hardcoding SQL joins, use configurations stored in metadata files.

Example Metadata Configuration:

- table: silver_bookings
  alias: bookings
  columns:
    - booking_id
    - total_amount
- table: silver_listings
  alias: listings
  join_condition: bookings.listing_id = listings.listing_id

With metadata-driven pipelines, adding tables or modifying joins becomes a matter of updating metadata, not rewriting SQL.

Step 3: Implementing Slowly Changing Dimensions (SCD Type 2)

What Is SCD Type 2?

SCD Type 2 tracks changes in dimension records over time while preserving historical data. For example:

• If a supplier changes locations, the old location remains in the table with a valid end date, while the new location has a future end date.

DBT Snapshots for Automating SCD Type 2 DBT provides snapshots to create SCD Type 2 without manual coding.

Snapshot Configuration Example:

snapshots:
  - name: dim_bookings
    target_database: my_database
    target_schema: gold
    unique_key: booking_id
    strategy: timestamp
    updated_at: updated_at_column

Key Concepts:

• Start/End Dates: Automatically track when changes occur.
• History Preservation: Retain older versions of data for auditing and historical analysis.

Step 4: Testing and Validation

To ensure data reliability, use DBT’s testing framework:

1. Singular Tests: Write SQL queries that validate specific conditions. For instance:

   select count(*)
   from {{ source('staging', 'bookings') }}
   where booking_amount < 0
   

   This query ensures no invalid negative booking amounts exist.
2. Generic Tests: Validate column-level constraints, such as primary keys or non-null requirements.

Run tests via:

dbt test

Step 5: Deploy and Showcase

Once your pipeline is complete, push your DBT project to a version control system like GitHub or GitLab. Highlight the following:

• Metadata-driven pipelines with automated joins
• Incremental data loads
• SCD Type 2 implementation
• Clear test coverage and validation

These features reflect real-world, industry-relevant practices and make your portfolio stand out.

Key Takeaways

• Master Medallion Architecture: Understand the purpose of bronze, silver, and gold layers in transforming raw data into analytics-ready datasets.
• Automate with Metadata: Avoid hardcoding SQL queries by building metadata-driven pipelines for scalability.
• Use Ginga Templates: Enhance DBT workflows with reusable, dynamic SQL templates.
• Implement Incremental Loading: Save resources by loading only new or changed data.
• Build SCD Type 2: Use DBT snapshots to manage historical changes in dimension tables.
• Test Your Data: Validate data quality using DBT’s singular and generic tests.
• Centralize Analytics: Combine data into a single source of truth (OBT) or star schema, depending on project needs.

Conclusion

Creating an end-to-end pipeline with DBT, Snowflake, and AWS is a comprehensive process that blends raw data ingestion, transformation, and model building into a cohesive workflow. By mastering these techniques, you position yourself as a skilled data engineer capable of handling real-world challenges.

This project not only equips you with technical skills but also teaches you how to approach complex data problems with innovative, scalable solutions. As the demand for data engineers grows, understanding how to implement metadata-driven pipelines, incremental loading, and SCD Type 2 will give you a competitive edge in the industry. Happy learning!

Source: "Airbnb End-To-End Data Engineering Project (For Beginners) | DBT + Snowflake + AWS" - Ansh Lamba, YouTube, Dec 28, 2025 - https://www.youtube.com/watch?v=3SZSDKEZqoA