Explore popular use cases to accelerate your development

Discover how to streamline ad hoc analysis with DBT and Python, solving common challenges like working with raw data, unnesting JSON arrays, and creating clear visualizations. This video shows you how to transform complex datasets into actionable insights quickly and efficiently, making data analysis more intuitive and impactful. Perfect for tackling real-world problems and improving your workflow!

This video show steps to enable development for dbt Cloud projects in VSCode with the help of Power User extension.

Query explanation is invaluable to understanding a complex piece of dbt or SQL code (especially written by others!). You can ask follow-up questions in the chat window, like - "Explain this function with some sample data" as well.

Many times, you need to add additional columns or change transformation in your dbt Model. Just ask for the changes in natural language, and DataPilot can generate the dbt model update code in VSCode itself.

Do you want to translate SQL queries from one dialect (SQL Server) to another (Snowflake)? Look no further; this video will show you how to do it easily. There are 15 different SQL dialects supported, and the DataPilot provides a translation explanation.

Generate code for your dbt tests, and edit and delete those tests quickly in VSCode UI without touching the YAML files in the dbt project.

Use dbt Column Lineage to understand the impact of your changes and troubleshoot dbt Models.

Deferring to prod functionality in dbt saves data teams a lot of time and money. The Power User extension makes it easy to use this functionality in local and SaaS modes.

Generate descriptions for your dbt models and columns in different languages and as per personas.

Collab functionality enables you to discuss code and documentation easily with the stakeholders via VSCode and UI. Many stakeholders are not comfortable using IDE directly; this functionality enables them also to have a discussion with technical users.