Introduction

Why Should You Care About dbt™?

If you've ever spent hours cleaning SQL queries, dealing with outdated dashboards, or wondering if your data is actually correct, you're not alone.

Data teams today often spend more time fixing broken pipelines than analyzing data:

• SQL transformations are scattered across multiple BI tools.

• Data frequently changes, leading to outdated and inaccurate reports.

• No version control means inconsistent business logic.

dbt™ solves this by centralizing data transformations inside the data warehouse and bringing software engineering best practices to analytics workflows.

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What is dbt™?

dbt™ (Data Build Tool) is a SQL-based transformation framework that enables data teams to transform data inside their warehouse using modular, version-controlled, and testable SQL models.

Unlike traditional Extract, Transform, Load (ETL) tools that apply transformations before loading data, dbt™ follows an Extract, Load, Transform (ELT) model, leveraging the power of the data warehouse for transformations.

With dbt™, you:

✅ Write simple SQL SELECT statements—dbt™ takes care of execution. ✅ Use Jinja to make SQL dynamic and reusable. ✅ Automate testing and documentation to keep data clean and transparent. ✅ Store every change in Git version control for full traceability.

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Before dbt™ vs. With dbt™

Without dbt™	With dbt™
SQL transformations are scattered across multiple BI tools, spreadsheets, or scripts.	All transformations are centralized within the data warehouse.
No version control—difficult to track changes.	Git-based version control ensures full transparency.
Manual SQL execution, increasing human error.	dbt™ automates and schedules transformations.
Limited or no automated testing.	Built-in testing ensures data quality before reporting.

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Where dbt™ Fits in the Data Stack

dbt™ acts as the transformation layer in the modern ELT data stack.

Extract & Load	Fivetran, Airbyte	Get raw data into the warehouse (ex. Snowflake, Bigquery)
Transform	dbt™	Convert raw data into analytics-ready models.
Analyze	Looker, Tableau	Query and visualize transformed data.

dbt™ does not handle data extraction or loading—it works after data is in the warehouse.

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How dbt™ Works

Once raw data is loaded into the warehouse, dbt™ applies transformations in a structured and scalable way.

1. Write SQL models – Define how data should be cleaned, joined, or aggregated.

2. Use Jinja for Dynamic SQL – Write parameterized and reusable SQL models.

3. Compile SQL – dbt™ converts models into optimized SQL queries.

4. Execute Transformations – The SQL runs inside your data warehouse.

5. Test Your Data – dbt™ validates data integrity, business rules, and referential consistency.

6. Generate Documentation – Model descriptions and dependencies are automatically documented.

7. Deploy Confidently – Version control ensures every change is tracked and reviewed before deployment.

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SQL Transformations with Jinja

dbt™ extends SQL using Jinja, a templating engine that makes SQL dynamic and reusable. Instead of writing the same logic multiple times, you can use Jinja macros, loops, and conditionals.

Using Jinja variables to make SQL dynamic

SELECT *  
FROM orders  
WHERE order_date >= '{{ var("start_date") }}'

Calling a Jinja Macro in a SQL Query

SELECT * FROM {{ active_customers() }}

Why it matters:

✅ Reduces redundant code (no need to rewrite logic). ✅ Ensures consistency across teams by enforcing the same transformation rules, reducing errors caused by inconsistent SQL logic. ✅ Makes SQL models easier to maintain by centralizing transformations instead of repeating logic in multiple places.

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Materialization: How dbt™ Stores Transformed Data

Materialization determines how dbt™ stores transformed data in the warehouse. Some transformations result in temporary views, while others persist as physical tables. The choice of materialization impacts performance, storage, and query speed. dbt™ supports multiple materialization strategies, which define how transformed data is stored:

• Tables – Creates a physical table that stores query results, refreshing only when the model is re-run.
• Views – Creates a virtual table that runs the underlying query each time it's accessed, without storing data.
• Incremental – Updates only new or changed data instead of rebuilding the entire table.
• Ephemeral – Runs queries at runtime using CTEs without creating permanent objects.

By combining Jinja and materialization, dbt™ optimizes data transformations while keeping SQL simple and scalable.

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Ensuring Data Quality with dbt™ Testing

Maintaining accurate, clean data is critical for reliable reporting. dbt™ provides built-in tests to catch data issues before they affect downstream analytics.

Types of Tests:

• Schema Tests: Validate uniqueness, null values, and referential integrity.

• Custom Tests: Check business logic, such as ensuring order dates always come before shipment dates.

• Row-Level Tests: Use Jinja to create dynamic, condition-based tests.

Example: A Row-Level Test to Check for Negative Order Totals

Edittests:
  - dbt_expectations.expect_column_values_to_be_between:
      min_value: 0
      max_value: 10000
      column_name: order_total

Why it matters:

✅ Prevents incorrect data from reaching dashboards, avoiding misleading business decisions. ✅ Saves analysts time by automating quality checks, reducing the need for manual data validation. ✅ Ensures data reliability so stakeholders can trust reports and analytics.

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Built-in Documentation

dbt™ automatically generates clear, searchable documentation for every model.

• Tracks model dependencies and lineage.

• Provides column descriptions for transparency.

• Stores metadata in a shareable UI.

This ensures that data teams always understand their transformations.

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Version Control & Collaboration

dbt™ integrates with Git, allowing teams to track and review every change before deployment.

• Every transformation is stored in version control for full auditability.

• Team members can collaborate through pull requests before merging changes.

• CI/CD workflows enable automated testing and deployment.

This eliminates conflicting SQL logic and ensures consistency across teams.

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The dbt™ Workflow

1. Development

Analytics Engineers write SQL models, use Jinja macros for flexibility, and test transformations locally before pushing changes.

2. Version Control

All changes are committed to Git, where pull requests enable team collaboration and code reviews.

3. Deployment

Once approved, models are built in production, tests are executed, and documentation is updated automatically.

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Common Use Cases

1. Data Warehousing

• Clean and standardize raw data for business users.

• Create dimension and fact tables.

• Maintain data integrity and governance.

2. Business Intelligence

• Ensure consistent reporting metrics across teams.

• Prepare data for dashboards and self-service analytics.

• Maintain a single source of truth for KPIs.

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Getting Started

To start using dbt™, you'll need:

Prerequisites

✔ A supported data warehouse (e.g., Snowflake, BigQuery, Redshift). ✔ Raw data loaded into the warehouse (dbt™ does not extract or load data). ✔ Basic SQL knowledge ✔ Familiarity with Git version control.

Want to start using dbt™ in Paradime for free? Check out our Paradime 101 guide to set up your first dbt™ project!