If you are a data analyst or engineer looking to elevate your data transformation game, you’ve landed on the right page! This blog will guide you through the process of setting up and using the dbt-databricks adapter, a powerful tool that unifies practices across data workflows.

What is dbt-databricks?

Before diving into the installations and setups, it’s essential to understand what dbt and Databricks bring to the table. dbt allows analysts to transform their data using sophisticated software engineering practices, while Databricks Lakehouse, provides a unified platform to manage all your data and AI workloads. The dbt-databricks adapter bridges these two tools seamlessly.

Key Features of dbt-databricks

• Easy Setup: No ODBC driver installation needed — just pure Python APIs.
• Open by Default: Use the performant Delta table format, enabling MERGE as the incremental strategy.
• Support for Unity Catalog: The adapter supports Unity Catalog’s 3-level namespace for better data organization.
• Performance Optimization: Automatically accelerated SQL expressions via the native, vectorized Photon execution engine.

Choosing Between dbt-databricks and dbt-spark

If your project is exclusively on Databricks, opt for dbt-databricks. For environments where you need support for both Databricks and Apache Spark, consider using dbt-spark, which remains actively developed.

Getting Started

Installation

To install the dbt-databricks adapter, simply run:

pip install dbt-databricks

If you ever need to upgrade to the latest version, use:

pip install --upgrade dbt-databricks

Profile Setup

Configuring your profile is crucial. Here’s how you can set it up:

your_profile_name:
  target: dev
  outputs:
    dev:
      type: databricks
      catalog: [optional catalog name]
      schema: [database schema name]
      host: [your.databricks.host.com]
      http_path: [sql http path]
      token: [your databricks token]

Quick Starts

To help you get going with the dbt-databricks adapter, here are some useful resources:

• Developing your first dbt project
• Using dbt Cloud with Databricks (Azure) | AWS
• Running dbt production jobs on Databricks Workflows
• Using Unity Catalog with dbt-databricks
• Using GitHub Actions for dbt CI/CD on Databricks
• Loading data from S3 into Delta using the databricks_copy_into macro

Troubleshooting

If you encounter any issues, consider the following troubleshooting ideas:

• Check your Python version; dbt-databricks is compatible with Python 3.7 and above.
• Ensure you are using Databricks SQL and runtime releases of 9.1 LTS or later.
• Validate your profile configurations to ensure the host, token, and other settings are correct.
• For any technical difficulties, feel free to reach out to the community and check for similar issues.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Tips and Tricks

Need a specific compute for a Python model? Override the compute by setting the http_path property in your model configuration, useful for running Python models on different clusters.

def model(dbt, session):
    dbt.config(
      http_path='sqlprotocolv1...'
    )

Conclusion

Getting started with dbt-databricks is an exciting journey into data transformations. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.