Updated – Unlock the Potential of Your Data in Vertica with dbt: Experience ETL-Free Data Transformation

Posted August 25, 2023 by Amrita Akshay, Information Developer

Vertica by opentext logo, a plus sign, and dbt logo on a blue background

Co-authored with Oussama Chakri.

Update – The upgraded dbt-vertica adapter is now available! It supports dbt-core version 1.5.0 with Vertica 23.3. We added new features, and the adapter works well with the latest Vertica version. Check it out!

Discover the all-new open-source dbt-Vertica adapter, offering cutting-edge features that will transform your data analytics journey. By connecting to Vertica with vertica-python, this adapter elevates the Data Build Tool (dbt) experience, enabling you to extract valuable insights and make data-driven decisions faster than ever.

With the dbt-Vertica adapter, you can eliminate the need for transferring data between tables within the same database. dbt will manage your data pipeline from raw data to aggregated data without ever leaving your database, streamlining your entire process.

dbt is a data transformation and analytics tool that simplifies data pipeline creation using SQL, Jinja, Macros, and YML while adhering to software engineering best practices. It offers benefits such as modular code structure, built-in version control, automated testing, and enhanced collaboration. With the new dbt-Vertica adapter, you can tap into the full potential of a modern data stack and bid farewell to traditional ETL processes.

Key Features:

  • Compatibility with dbt-core version 1.5.0 in accordance with DBT guidelines
  • Incremental model strategies ‘append’ and ‘insert_overwrite’ support
  • Configuration support for the merge strategy parameter ‘merge_update_columns’
  • Multiple optimization parameters for Vertica’s table materialization
  • Default-enabled privileges inheritance for model materialization using INCLUDE SCHEMA PRIVILEGES
  • Pre-configured profile_template to assist users in creating a profile while setting up the project
  • Python 3.11 support
  • Added support for ‘constraints’ data structure. When enforced, a constraint guarantees that you will never see invalid data in the table materialized by your model.

Get started by downloading the adapter from the GitHub page. We are committed to enhancing the adapter’s capabilities and encourage users to contribute and collaborate on this project.

For detailed information on setting up and configuring the adapter, refer to the Vertica setup and Vertica configurations.

Explore the solution guide and watch the video tutorial here:
End-To-End Data Transformation Guide: Using the dbt-vertica Adapter for Analytics

Unlock the power of your data in Vertica with dbt, and experience the unmatched speed of Vertica’s query processing and massive parallel processing capabilities. Say goodbye to ETL and hello to a new era of data transformation!