The dbt project at tails.com has over 600 models and 66k lines of code. With multiple contributors to a project and varying SQL backgrounds, it's really difficult to maintain consistent readability and comprehension across a codebase like that by hand.

Python has flake8, Javascript has JSLint, but SQL...?

Listen to this talk from Alan Cruickshank to find out whether SQLFluff might help your teams be more productive with SQL.

In this introduction to dbt tutorial, you'll to learn about the core concepts of dbt and how it's used.

You probably know that data is a huge part of how the world runs now, including how businesses report on metrics and how they operate.

One of the difficult parts of working with data is communicating enough context and information to everyone in the organization so they understand the data they're looking at and whether it answers their questions.

That's where dbt comes in. dbt is a data transformation and documentation tool that helps data analysts, data engineers, and business stakeholders collaborate on data.

This introduction to dbt will walk you through: a short history of ELT, what is dbt (data build tool), and dbt core concepts.

The core dbt concepts include: - Expressing transforms with SQL select - Automatically build the DAG with ref(s) - Tests ensure model accuracy - Documentation is accessible and easily updated - Use macros to write reusable SQL

Learn more about dbt at: https://getdbt.com https://twitter.com/getdbt

Learn more about dbt Labs (formerly Fishtown Analytics) at: https://www.getdbt.com/dbt-labs/about-us/ https://twitter.com/dbt_labs https://www.linkedin.com/company/dbtlabs