Mastering dbt Deployment: Best Practices and Strategies for Different Environments

This is some text inside of a div block.
May 13, 2024

When deploying dbt (Data Build Tool) in different environments, it is crucial to follow best practices and strategies to ensure efficient and effective management of the deployment process. This tutorial will guide you through the key considerations and steps for deploying dbt in various environments, setting up development environments, and leveraging features like dbt Explorer and smarter metadata creation.

What is dbt?

dbt (Data Build Tool) is an open-source data transformation tool that helps data teams transform raw data into clean, structured, and reliable data sets for analytics purposes. It enables data teams to write, test, and deploy SQL-based data transformations, ensuring data quality and consistency across different environments.

Best Practices for Deploying dbt in Different Environments

Following best practices can help ensure successful deployment of dbt in different environments:

  • Segment jobs across environments based on their purposes, such as Production and Staging/CI.
  • Build production-grade models into a different schema and database to experiment without affecting production data.
  • Use dedicated credentials for production runs and mark one environment as the Production environment in dbt Cloud.

How to deploy dbt in a multi-environment setup

Deploying dbt in different environments requires adherence to best practices such as environmental segmentation, building production-grade models into different schemas and databases, and specifying a production environment in dbt Cloud.

1. Environmental Segmentation

Environmental segmentation is important when deploying dbt to prevent changes made during development from accidentally impacting downstream users. This approach allows for a more mature and scalable view of the world, enabling features like dbt Explorer and revised CI workflows.

2. Building Production-Grade Models into Different Schemas and Databases

Building production-grade models into a different schema and database allows for experimentation without affecting production data. It also enables the elimination of false positives and overbuilding of models in CI, providing a more up-to-date understanding of the project.

3. Specifying a Production Environment in dbt Cloud

Marking one environment as the Production environment in dbt Cloud allows for smarter metadata creation and is crucial for features like dbt Explorer and revised CI workflows. This separation of production workloads enables more efficient management of deployments in multiple environments.

How To Set up Development Environments in dbt

Setting up development environments in dbt involves various strategies, including using different schemas within one data warehouse to separate environments and having one database per environment with a schema per developer in the development database. These strategies aim to provide developers with dedicated spaces for development, ensuring efficient management of the development process.

Use Different Schemas within One Data Warehouse

Using different schemas within one data warehouse helps in separating development environments, providing each user with their own development environment. This approach ensures that developers have dedicated spaces for development, preventing interference with each other's work.

Have One Database per Environment with a Schema per Developer in the Development Database

Having one database per environment with a schema per developer in the development database offers developers dedicated environments for their work. This strategy ensures that each developer has their own space for development, contributing to efficient management of development environments.

Common Challenges and Solutions

Deploying dbt in different environments may present some challenges, such as managing costs, ensuring data consistency, and maintaining a balance between development and production environments. Here are some solutions to these challenges:

  • Limit data processed in development environments to manage costs.
  • Utilize cloning techniques to create a 1:1 copy of the production environment for development purposes.
  • Implement strict version control and testing procedures to maintain data consistency across environments.

Further Learning

To dive deeper into deploying dbt in different environments, consider exploring the following topics:

  • dbt Cloud: Learn more about dbt Cloud and its features for managing multi-environment deployments.
  • CI/CD pipelines: Understand how continuous integration and continuous deployment pipelines can help manage dbt deployments across environments.
  • Data Warehousing: Investigate different data warehousing solutions and their compatibility with dbt deployments.
  • Data Quality and Compliance: Learn about Secoda's dbt Integration and how it helps ensure data quality, accuracy, and consistency across data sets while maintaining compliance with security standards.

Keep reading

See all