dbt-fal VS dbt-utils

Happy data scientists use fal and dbt

fal (https://github.com/fal-ai/fal) helps with this! In fact we wrote a blog post about feature engineering with fal and dbt recently

We built a dbt adapter that helps you run local Python code with your dbt project with any other data warehouse. You can see it here: https://github.com/fal-ai/fal/tree/main/adapter

This new adapter helps you run your dbt Python models with isolated Python environments using our open source library: https://github.com/fal-ai/isolate

Have you considered fal? https://github.com/fal-ai/fal

Something to maybe comment on the post is that dbt is introducing Python transformations on the data warehouse offering (e.g. Snowspark) soon and that there are tools like fal that enable these Python transformations to run in a different environment which you have control over.

Fal-AI ( https://github.com/fal-ai/fal ) Fal helps to run Python scripts directly from the dbt project. For example, you can load dbt models directly into the Python context which helps to apply Data Science libraries like SKlearn and Prophet in the dbt models. This especially improves the data science capabilities within a data pipeline. What I extremely like about fal is that it extends dbt from a interesting angle.

- 🐍 fal ai: Fal helps to run Python scripts directly from the dbt project. For example you can load dbt models directly into the Python context which helps to apply Data Science libaries like SKlearn and Prophet in the dbt models.

I haven’t tried it yet but I know https://fal.ai/ helps you run python alongside dbt.

Yes I agree with you that having fivetran/airbyte and dbt covers a lot of the airflow use cases.. That being said you might still want to run some scripts after the DBT transformation is over, we ran into this exact problem and built a useful CLI tool for running python scripts alongside the dbt run.

Great write-up! For your logging integration, you might have a look at fal. There's an example of sending events to Datadog

// To get around this, we can use the approach outlined by how dbt does ansi sql generate_series

  // https://github.com/dbt-labs/dbt-utils/blob/main/macros/sql/generate_series.sql

I got bit by dbt-utils/deduplicate naively removing any row that contained a null in it recently, but fortunately there was a workaround for Databricks and a few other flavors of SQL.

I'm used to utilising dbt and defining my tests there (along with dbt-utils or https://github.com/calogica/dbt-expectations): I simply add a list item to a column definition and can already define a great number of tests without having to copy code. I can even extend the pre-defined using generic tests. Writing custom tests also integrates nicely. Additionally it's very convenient to tag tests or define a severity. The learning curve for a business engineer is almost flat as long as they know some SQL.

My top three:

- Dev/stag/prod env check numbers before pushing to production.

- Unions between two sources that are not the same shape can be done without the headache. https://github.com/dbt-labs/dbt-utils#union_relations-source

- Macros for common case when statements.

Add tests: unit tests in SQL are still not really practical, but testing the data, before allowing users to see it, is possible. dbt has some basic tests like Non-NULL and so on. dbt_utils supports comparing data across tables. If you need more, there is Great Expectation and similar tools. dbt also supports writing SQL queries which output “bad” rows. Use this to, e.g. check a specific order against manually checked correct data. Tests give you confidence that your pipelines produce correct results: nothing is worse than waking up with a Slack message from your boss that the graphs look wrong… They are especially useful in case you have to refactor a data pipeline. Basically every query you would run during the QA phase of a change request has a high potential to become an automatic test.

The equality test macro is also in the dbt-utils package from fishtown at https://github.com/fishtown-analytics/dbt-utils/blob/master/macros/schema_tests/equality.sql