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dbt on Databricks.

Context and Motivation

  • dbt (Data Build Tool): A popular open-source framework that organizes SQL transformations in a modular, version-controlled, and testable way.
  • Databricks: A platform that unifies data engineering and data science pipelines, typically with Spark (PySpark, Scala) or SparkSQL.

The post explores whether a Databricks environment—often used for Lakehouse architectures—benefits from dbt, especially if a team heavily uses SQL-based transformations.

2. Local Setup With dbt + Databricks

  1. Install dbt-databricks

    bash
    pip install dbt-core dbt-databricks

    This adapter enables dbt to connect to Databricks clusters or SQL Warehouses.

  2. Initialize a dbt Project

    bash
    dbt init --profiles-dir . <project_name>
    • You will be prompted for Databricks connection details:
      • Host (e.g., https://dbc-xxxx.cloud.databricks.com)
      • HTTP Path (from the Databricks SQL/ODBC settings)
      • Personal Access Token (Databricks token)
      • Catalog and Schema if using Unity Catalog

  3. Validate the Connection

    bash
    dbt debug
    • Checks the profiles.yml and confirms connectivity to Databricks.

3. Writing and Running dbt Models in Databricks

  1. Default Materializations: By default, dbt models become “views.” You can override this in your model’s config to store them as tables, views, incremental tables, ephemeral CTEs, or materialized views.

  2. Example dbt Model (SQL file in models/ directory):

    sql
    {{
    config(
    materialized = "table",
    file_format = "delta"
    )
    }}

    WITH source_data AS (
    SELECT date, model, COUNT(failure) AS failure_count
    FROM confessions.default.hard_drive_failures
    GROUP BY date, model
    )
    SELECT *
    FROM source_data

    • This snippet shows how to define a table using SparkSQL syntax on Databricks, storing data in Delta format.

  3. Execute the Model:

    bash
    dbt run --model models/hardware.sql
    • dbt compiles the SQL, connects to Databricks, and creates or updates the table in Unity Catalog as specified.

4. Production Considerations

  1. Typical Deployment:

    • Store dbt code in a Git repository.
    • Use Databricks Jobs (or your existing orchestration) to clone, run, and schedule the dbt project.
    • Optionally integrate dbt runs with CI/CD pipelines for version control and continuous testing.

  2. Performance / Storage Implications:

    • If your data is huge (hundreds of TBs), pay attention to materialization strategies so you don’t inadvertently create large copies.
    • Incremental models help mitigate full refresh overhead.

  3. Why dbt for Databricks?

    • If your team is SQL-first, dbt provides:
      • Reusable SQL modules
      • Built-in testing (tests/)
      • Version control and CI integration
    • If your team already uses PySpark/Scala extensively (e.g., for ML pipelines), dbt might offer fewer advantages since you already have modular, testable code in a programming language.

5. My Take

  • Ease of Use: The dbt-databricks integration “just works,” making local development straightforward.
  • Best Fit: Primarily for teams that rely on SQL transformations in Databricks—and want the reproducible, testable workflow that dbt offers.
  • Alternative Approach: Advanced Spark use cases (ML, complex transformations) may not gain as much from dbt’s SQL-centric approach.

Bottom Line

dbt on Databricks is simple to set up and can be highly effective if your workloads are SQL-driven. The combination provides robust data modeling, version control, and testing for Lakehouse transformations. However, teams that are heavily code-oriented (PySpark, Scala) for advanced pipelines may not see the same return on rewriting everything in SQL.