ETL/ELT with dbt for software teams — Real‑World Case Study — Practical Guide (Oct 1, 2025)

ETL/ELT with dbt for software teams — Real‑World Case Study

Level: Intermediate

As of October 1, 2025, dbt (data build tool) has become an essential tool for modern software teams looking to streamline their data transformation workflows in a clean, maintainable, and scalable way. This article walks through a real-world case study demonstrating how to implement ETL/ELT pipelines using dbt, focusing on practical steps, avoiding common pitfalls, and validation best practices.

Why dbt for ETL/ELT?

dbt is primarily an ELT tool designed to transform raw data inside your cloud data warehouse using SQL. Unlike traditional ETL tools, dbt leverages the power of your database for transformation, maintaining a single source of truth, and enabling software teams to adopt software engineering best practices such as version control, modularity, and testing.

Typical data warehouses supported by dbt include Snowflake, BigQuery, Redshift, and Databricks (>= dbt v1.0.0). Choose dbt ELT workflows over traditional ETL when you want to simplify pipelines, reduce custom code, and foster collaboration between data engineers and analysts.

Prerequisites

Intermediate SQL skills
Basic familiarity with dbt (version 1.6.x or later recommended for stability and features)
Cloud data warehouse access (Snowflake, BigQuery, Redshift, or Databricks)
Git repository to manage dbt projects
Command-line environment with Python 3.9+ installed
dbt CLI installed (pip install dbt-core plus your adapter, e.g., dbt-snowflake)

Hands-on Steps

Step 1: Initialise a new dbt project

Use the following CLI command to create a new dbt project skeleton:

dbt init my_project

This scaffolds a project directory with configurations in dbt_project.yml and a sample models/ folder.

Step 2: Configure your warehouse connection

Modify the profiles.yml file (usually located in ~/.dbt/) to supply credentials for your environment. For example, a Snowflake config looks like:

my_project:
  target: dev
  outputs:
    dev:
      type: snowflake
      account: your_account
      user: your_user
      password: your_password
      role: your_role
      database: your_database
      warehouse: your_warehouse
      schema: your_schema
      threads: 4
      client_session_keep_alive: False

Step 3: Develop modular SQL transformation models

Create models in models/. The principle is to build layered transformations that are easily testable and reusable. For example, processing raw clickstream data might look like:

-- models/stg_clickstream.sql

with raw_clicks as (

    select
        event_id,
        user_id,
        event_timestamp,
        event_type,
        page_url

    from raw.clickstream_data

)

select
    event_id,
    user_id,
    event_timestamp,
    event_type,
    page_url
from raw_clicks
where event_type in ('page_view', 'click')

Use Jinja templating for macros and DRY SQL principles.

Step 4: Implement data testing and documentation

Add schema tests to ensure data quality:

version: 2

models:
  - name: stg_clickstream
    columns:
      - name: event_id
        tests:
          - not_null
          - unique
      - name: user_id
        tests:
          - not_null

Use dbt test to run built-in tests and customise your own. Ensure your models are documented with descriptions in .yml files to help maintainability.

Step 5: Build and run your transformations

Execute your workflow using:

dbt run

To run tests and validate data:

dbt test

Use dbt docs generate and dbt docs serve to create and view documentation.

Common Pitfalls

Connection handling: Incorrect credentials or insufficient permissions to read/write schemas cause failures. Always verify with your DBA.
Model dependencies: Circular dependencies between models will break builds. Use ref() properly to manage dependencies.
Lack of testing: Skipping tests leads to silent data quality issues. Start small and build up test coverage incrementally.
Version drift: Infrequent updates to dbt or warehouse adapters may cause unexpected behaviour. Pin versions and upgrade cautiously.
Overcomplicating transformations: Complex SQL in one model reduces readability; split logic into layered models.

Validation Best Practices

Validation steps ensure reliability and trust in your ELT pipelines:

Run schema tests regularly: Automate dbt test as part of your CI/CD pipeline.
Cross-check row counts: Compare input and output row counts after transformations to detect unexpected data loss.
Sample data reviews: Query samples of source and target data manually or with tools.
Continuous documentation: Keep your model docs updated to reflect transformation logic and assumptions.
Monitor performance: Use your warehouse’s native tools to monitor and optimise query execution times.

Checklist / TL;DR

Set up dbt project and configure warehouse credentials correctly.
Develop modular, layered SQL models using ref() for dependencies.
Implement and maintain data tests and documentation.
Run dbt run, dbt test, and dbt docs regularly.
Integrate dbt commands within CI/CD pipelines for automation.
Avoid circular references and overly complex transformations.
Keep dbt versions and adapters current but stable, using release notes to anticipate breaking changes.
Validate outputs with row counts, tests, and manual spot-checks.