Data Build Tool (dbt) is a powerful, SQL-based open-source tool. It helps organizations quickly build and maintain data transformations in their data pipelines. It is specifically designed for data analysts, engineers, and scientists who work with large amounts of data stored in data warehouses and other data storage systems.

Building data pipelines is challenging, as we all know. We require straightforward, efficient tools that construct maintainable pipelines. In this article, you will learn how to build data pipelines using dbt Databricks.

What is dbt?

Data Build Tool (dbt) is an open-source tool that helps data engineers and analysts transform raw data into actionable insights. dbt standardizes transforming and loading data into a data warehouse. This is done by automating tasks such as creating tables, running tests, and documenting the data. With dbt, users can write SQL transformations that are organized and versioned, making it easier to collaborate with others and keep track of changes to the data. 

Additionally, dbt provides a robust testing framework, allowing users to catch issues early in the process and improve the quality of their data. This helps to reduce the time and effort required to manually debug and validate data. It makies data teams more efficient and data more trustworthy. Overall, dbt simplifies the process of building, testing, and deploying data transformations, enabling organizations to get the most value from their data.

What is Databricks?

Databricks is a cloud-based platform that provides a collaborative environment for data engineers, data scientists, and business analysts to process big data, build and train machine learning models, and perform advanced analytics. It was created to make it easier to work with large amounts of data and to provide a single platform for data processing, machine learning, and AI.

Databricks provides a web-based interface for working with data and is designed to be used in a collaborative manner. It integrates with popular data storage systems such as Amazon S3, Microsoft Azure, and Google Cloud Storage. It also supports popular programming languages like Python, Scala, and SQL. The platform also provides built-in machine learning algorithms, visualization tools, and a library of pre-built models that can be used to quickly build and train models.

Building Data Pipelines with dbt Databricks

Dbt Databricks: Data pipeline
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Prerequisites

The first step is to install dbt using the pip command. You need to have Python >3.7 installed.

pip install dbt-databricks

This will also install dbt-core and any other dependencies available.

Set Up and Test the Connection

Both SQL endpoints and Databricks all-purpose clusters are accessible using dbt-databricks. SQL endpoints offer all of Spark’s flexibility, while Databricks offers a method of running SQL workloads at optimal price and performance.

To set up Databricks as a target, you can use the below code:

your_profile_name:
  target: dev
  outputs:
    dev:
      type: databricks
      catalog: [optional catalog name, if you are using Unity Catalog, is only available in dbt-databricks>=1.1.1]
      schema: [schema name]
      host: [yourorg.databrickshost.com]
      http_path: [/sql/your/http/path]
      token: [dapiXXXXXXXXXXXXXXXXXXXXXXX] # Personal Access Token (PAT)
      threads: [1 or more]  # optional, default 1

After this, follow these steps to create a dbt project and configure your connection profiles:

  • Run the dbt init command and name for your project. . A project called my dbt demo is created by this process.
  • When asked whether to use spark database or not, enter the number corresponding to a databricks database.
  • For host value:
    • Cluster: Enter the Server Hostname value from the Advanced Options, JDBC/ODBC tab for a cluster.
    • SQL Warehouse: Enter the Server Hostname value from the Connection Details tab.
  • For HTTP path value:
    • Cluster: Enter the HTTP Path value for your Databricks cluster in the Advanced Options, JDBC/ODBC tab.
    • SQL Warehouse: Enter the HTTP Path value for your SQL warehouse from the Connection Details page.
  • For the token, enter the value of Databricks Personal Access Token.
  • Enter the number that corresponds with the use Unity Catalog or not use Unity Catalog when asked for the desired value.
  • If you choose to use Unity Catalog, enter the desired value for the catalog.
  • When prompted, enter the required values for schema and threads.
  • Your entries are saved in a profiles.yml file by dbt. The output of the dbt init command lists this file’s location. The dbt debug —config-dir command can be used later to list this location. You can now open this file to examine the content and to verify it.
  • Run the dbt debug command to verify that the connection details are accurate.

Your output should be like this:

Bash:

dbt debug

Console:

...
Configuration:
  profiles.yml file [OK found and valid]
  dbt_project.yml file [OK found and valid]

Required dependencies:
 - git [OK found]

Connection:
  ...
  Connection test: OK connection ok

Why Use dbt Databricks?

As a development environment, dbt allows developers to transform data by writing SQL select statements. dbt then takes these statements and creates views and tables. Your code is compiled into raw SQL that runs on a Databricks database. This offers users features such as version control, modularity, collaborative coding, documentation, and more. 

The dbt-databricks adapter, which was developed by the creators of dbt-spark, contains all the code for dbt to work with Databricks. The key benefits of this adapter include the following:

  • No need to install additional drivers or dependencies for use on the CLI
  • Makes use of Delta Lake format for all models out of the box. For example, Merge is used as the default incremental materialization strategy.
  • SQL expressions are optimized to run with the Photon execution engine.

Best Practices for dbt Databricks

Here are some best practices for using dbt with Databricks:

  • Use version control: Store your dbt models, migrations, and seeds in a version control system such as Git.
  • Use Databricks data catalog: Dbt integrates with Databricks data catalog to make it easier to manage metadata.
  • Use environment variables: Use environment variables to manage sensitive data, such as connection strings and API keys.
  • Schedule dbt runs: Schedule dbt runs using Databricks Jobs to ensure your data is updated on a regular basis.
  • Use target-specific macros: Use target-specific macros for Databricks to optimize performance, such as using spark.sql instead of {{ sql() }}.
  • Monitor performance: Monitor performance using Databricks metrics and logging to ensure your dbt jobs are running efficiently.
  • Use test coverage: Use dbt’s testing functionality to ensure your models produce the expected results.
  • Collaborate with others: Encourage collaboration by sharing your dbt project and promoting code review.

Final Thoughts

In this article, you learned about building pipelines on dbt Databricks. You also read about some best practices for this and why we should use dbt Databricks.

For cases when you rarely need to replicate data, your engineering team can easily do it. For frequent and massive volumes of data transfers from multiple sources, your engineering team would need to constantly monitor and fix any data leaks. Or you can simply hop onto a smooth ride with cloud-based ELT solutions like Hevo Data which automates the data integration process for you and directly connects you to dbt for your transformations. Hevo Data also offers 150+ plug-and-play integrations and saves countless hours of manual data cleaning & standardizing.

Want to take Hevo for a spin? Sign up for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.

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