Data plays a vital role in business decision-making. As the amount of data increases, so does the need to make data accessible to everyone in the business. By transforming your data, you can integrate, cleanse, deduplicate, restructure, filter, aggregate, and merge your data – enabling your business to develop valuable and reliable insights through analysis and reporting. There are many tools on the market to help you with this, but a data build tool (DBT) in particular simplifies and speeds up the process of transforming data and building pipelines.

What is dbt [data build tool]?

According to dbt themselves, this is a development framework that combines modular SQL with software development best practices to make data transformation reliable, fast, and fun.

In short; dbt (data build tool) is developed to make it easier for data analysts and engineers to work with data, by offering a consistent and standardized approach to data transformation and analysis.

dbt allows users to define their data models using SQL and then uses those models to generate optimized SQL code that can be run against a data warehouse or other data storage system. This allows users to build a maintainable and scalable data infrastructure that can be easily updated and extended over time.

dbt compiles and runs your code against your data platform, allowing you and your team to collaborate on a single source of truth for metrics, insights, and business definitions. This single source of truth, combined with the ability to define tests for your data, reduces errors when logic changes and alerts you when problems arise.

In addition to generating SQL code, dbt also provides a number of features that make working with data easier. These features include the ability to manage dependencies between data models, run tests to ensure data integrity, and trace the data pipeline to understand how it has transformed over time.

Use-Cases for dbt

With DBT, data analysts take ownership of the entire analytics workflow, from writing code for data transformation all the way to distribution and documentation – in addition to becoming better at fostering a data-driven culture in the organization.

Some common use cases for DBT are:

• Build and maintain data pipelines: dbt can be used to define data models using SQL and then generate optimized SQL code that can be run against a data warehouse or other data storage system. This allows users to build and maintain a scalable data infrastructure.
  
• Ensure data quality and integrity: dbt offers a number of features that make it easier to ensure data quality and integrity. This includes the ability to run tests to validate data, as well as trace the data lineage to understand how it has changed over time.
• Standardization of data transformation processes: DBT provides a consistent and standardized approach to data transformation and data analysis, making it easier for data analysts and engineers to work with data. This can help organizations improve the quality and reliability of their data, making it easier to extract insights and drive business decisions.
• Provide a collaborative environment for data teams: DBT allows data analysts and engineers to work together on the same data models and transformations, providing a collaborative environment for data teams. This can help improve communication and collaboration within data teams and make it easier to work on complex data projects.

Two different products

You can access dbt with dbt Core or dbt Cloud .

dbt Core

dbt Core is an open source tool that enables data teams to define their data models using SQL and then use those models to generate optimized SQL code that can be run against a data warehouse or other data storage system.

dbt Cloud

dbt Cloud , on the other hand, is a cloud-based platform that provides additional features and functionality on top of dbt-core. dbt Cloud offers a web-based interface for managing data models, as well as additional features such as planning, collaboration tools, and integrations with other data tools.

dbt Cloud is built around dbt Core, but it also provides:

• Web-based user interface to make it more accessible
• Runs on infrastructure from dbt so it's faster to get started and less to manage

Additional features, such as:

• Integrated development environment
• Schedule jobs to run transformations regularly
• Integrations with other tools, such as Github or Azure DevOps
• Semantic layer for common definitions of key metrics across analysis tools

Summary

In conclusion, DBT is a powerful tool that can help organizations improve their data infrastructure and make it easier for data analysts and engineers to work with data. By providing a consistent and standardized approach to data transformation and analysis, DBT can help organizations improve the quality and reliability of their data, making it easier to extract insights and drive business decisions.

Get started with data and analytics today

We at Twoday are happy to help you with DBT. We are the Nordic region's leading competence center in business intelligence and advanced analysis. Whether you want to take a holistic approach or refine selected parts, we are ready to help you.