What it is: Data transformation tool that enables analytics engineers to transform data using SQL. Version control for data transformations with testing and documentation.

What It Does Best

SQL-first transformations. Write SELECT statements, dbt handles the CREATE TABLE. Version control and deploy like software.

Testing built-in. Data quality tests as code. Unique, not-null, relationships, custom tests. Catch bad data early.

Documentation automation. Auto-generate data lineage and documentation from your code. Keep docs in sync with reality.

Key Features

Jinja templating: DRY code with macros and variables

Incremental models: Process only new/changed data for efficiency

Data lineage: Automatic DAG visualization of dependencies

Tests and assertions: Built-in and custom data quality tests

Packages: Reusable transformation libraries from community

Pricing

dbt Core: Free, open source (run locally or on your infra)

dbt Cloud Developer: Free for 1 developer seat

dbt Cloud Team: $100/developer/month

dbt Cloud Enterprise: Custom pricing (SSO, advanced features)

When to Use It

✅ Transforming data in warehouse (Snowflake, BigQuery, Redshift)

✅ Analytics engineering team exists

✅ Need version control and testing for SQL

✅ Building data marts from raw data

✅ Want software engineering practices for data

When NOT to Use It

❌ Need to extract or load data (use Fivetran, Airbyte)

❌ Real-time streaming transformations (use Spark, Flink)

❌ Non-SQL transformations (use Airflow, Python)

❌ Very simple transformations (might be overkill)

❌ No data warehouse (dbt needs a target database)

Common Use Cases

Data modeling: Transform raw data into analytics-ready models

Business metrics: Define metrics once, use everywhere

Data quality: Test data integrity and freshness

Documentation: Self-documenting data warehouse

Incremental processing: Efficient updates of large tables

dbt vs Alternatives

vs Dataform: Similar tools, Dataform acquired by Google (BigQuery focused)

vs SQL in Airflow: dbt better for pure transformations, Airflow better for orchestration

vs stored procedures: dbt version controlled and testable, procedures harder to maintain

Unique Strengths

Analytics engineering focus: Purpose-built for data transformation workflow

Testing framework: Best-in-class data quality testing

Community packages: Pre-built transformations for common tasks

Documentation as code: Docs that stay up to date automatically

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