Data glossary
We can help you navigate the meaning of the most commonly used terms in the data and technology segment.
We’ve put together a glossary to help you all
terms to help you decipher them.
Using automated tools and processes to streamline data integration, especially in environments with a large volume of data.
Using statistical methods, machine learning and other analytical tools to uncover patterns, trends and information in data.
What is dbt?
dbt, or data build tool, is an open-source tool with a command-line interface that helps organizations build, test, and maintain their data infrastructure. It's kind of like Looker PDTs, but much more than that. It has similarities to Airflow, but is built for a completely different user. dbt is its own unique thing.
The dbt tool 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 expanded over time.
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 track the development line to understand how data has changed over time.
dbt and no-vendor lock: dbt fits well into the modern BI stack and works with products such as Snowflake, Keboola, BigQuery, Looker, Fivetran, Redshift and Mode. With dbt code (stored in Git repositories), developed code can be moved to another platform at any time.
For more information on the business use of dbt, visit our website here.
