Want to IMPLEMENT a new data stack at your company? Go here: https://bit.ly/kds-consulting
Want to LEARN skills to advance your data career? Go here: https://bit.ly/starter-mds

-----

You don't need to process every record in a table, every time.

Fortunately, dbt has a great solution for this scenario with their "incremental" materialization option.

When setup properly, they can help you significantly cut costs & processing time.

This is because incremental models only process new data vs rebuilding an entire table (the default setting).


But setting up incremental models isn't always straight forward.

It requires a few steps, an understanding of underlying functionality and some customization.

All that to say, I've noticed this process trips people up and therefore they put off implementing it.


So in today's video I'd like to help you out by covering:
- What incremental models in dbt are all about
- Step by step how to build one
- The process to add/update new data

Thank you for watching!


►► The Starter Guide for dbt (Free PDF)
Get clarity on key dbt concepts so you can build better projects & avoid common mistakes → https://bit.ly/starter-dbt



Timestamps:
0:00 - Intro
0:27 - What are incremental models?
1:45 - How to use is_incremental()
4:06 - Inserting new data
5:31 - Update existing data
6:57 - Handling schema changes
9:39 - Using the --full-refresh flag

Title & Tags:
How to Build Incremental Models | dbt tutorial
#kahandatasolutions #dataengineering #dbt