dbt-fal VS lightdash

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

> YAML, pivoting being done in the frontend, no symmetric aggregates

(one of the maintainers of Lightdash) You touched on some of our most interesting problems here! Would be especially interested to hear about what you liked / didn't like about symmetric aggregates in Looker and how you find dev with YAML. If you have an idea of how you'd like these to look in Lightdash, the team would be really open to making that a reality.

For pivoting in the backend, this is coming! Issue here: https://github.com/lightdash/lightdash/issues/2907

What are the 5 hottest dbt Repositories one should star on Github 2022?

dbt is a software framework that sits in the middle of the ELT process. It represents the transformative layer after loading data from an original source. Dbt combines SQL with software engineering principles.

Here are my top5!

- Lightdash (https://github.com/lightdash/lightdash): Lightdash converts dbt models and makes it possible to define and easily visualize additional metrics via a visual interface.

- ⏎ re_data (https://github.com/re-data/re-data): Re-Data is an abstraction layer that helps users monitor dbt projects and their underlying data. For example, you get alerts when a test failed or a data anomaly occurs in a dbt project.

- evidence (https://github.com/evidence-dev/evidence): Evidence is another tool for lightweight BI reporting. With Evidence, you can build simple reports in "medium style" using SQL queries and Markdown.

- Kuwala (https://github.com/kuwala-io/kuwala): With Kuwala, a BI analyst can intuitively build advanced data workflows using a drag-drop interface on top of the modern data stack without coding. Behind the Scenes, the dbt models are generated so that a more experienced engineer can customize the pipelines at any time.

- 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.

Lightdash ( https://github.com/lightdash/lightdash ) Lightdash converts dbt models and makes it possible to define and easily visualize additional metrics via a visual interface. The front end helps to understand and extend the underlying SQL queries. Lightdash also visualizes business metrics and makes them shareable with the data team. It is also possible to integrate all data into another visualization tool.

- ⚡️ Lightdash: Lightdash converts dbt models and makes it possible to define and easily visualize additional metrics via a visual interface.

Visualization / Analysis: Lightdash, Superset

Ingestion / ETL: Airbyte, Singer, Jitsu Transformation: dbt Orchestration: Airflow, Dagster Testing: GreatExpectations Observability: Monosi Reverse ETL: Grouparoo, Castled Visualization: Lightdash, Superset

1) An integration with Metabase Cloud is on our roadmap for Q1! We'd love to integrate with Lightdash, but they don't have a public API just yet[1].

2) Several of our customers use us to alert on schema changes in Postgres, specifically so they can get ahead of application database changes that will end up in the warehouse, so you're definitely not alone! Here's a link on how to connect postgres: https://docs.metaplane.dev/docs/postgres

That's an excellent stack and one we kept front and center when building out Metaplane, so definitely let us know if you have any feedback or suggestions here!

[1]: https://github.com/lightdash/lightdash/issues/632

I would recommend lightdash which is essentially an open source Looker clone https://github.com/lightdash/lightdash