dbt-fal
isolate
| dbt-fal | isolate | |
|---|---|---|
| 12 | 2 | |
| 851 | 71 | |
| - | - | |
| 7.7 | 7.5 | |
| about 1 month ago | 7 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dbt-fal
-
machine learning in snowflake, unhappy data scientists
Happy data scientists use fal and dbt
-
dbt for ML Engineering
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
-
Dbt-fal: a dbt Python adapter with local code execution
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
-
Data Stack for Python Scripts (and other transformations)
Have you considered fal? https://github.com/fal-ai/fal
-
Comparing dbt with Delta Live Tables for doing transformations
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.
-
What are the hottest dbt Repositories you should star on Github 2022? - Here are mine.
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.
-
What are your hottest dbt repositories in 2022 so far? Here are mine!
- 🐍 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.
-
Wanting to move away from SQL
I haven’t tried it yet but I know https://fal.ai/ helps you run python alongside dbt.
-
Do I need orchestration for a Fivetran-dbt stack?
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.
-
Why is Data Build Tool (DBT) is so popular? What are some other alternatives?
Great write-up! For your logging integration, you might have a look at fal. There's an example of sending events to Datadog
isolate
-
Dbt-fal: a dbt Python adapter with local code execution
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
-
Easiest way to run dbt python models - announcing dbt-fal
- dbt-fal can run your Python code locally and in the cloud - dbt-fal provides easy environment management and isolation between models, backed by https://github.com/fal-ai/isolate - dbt-fal lets you run code in the same Python environment in the cloud and locally - dbt-fal supports all warehouses and databases, including Postgres Redshift and more
What are some alternatives?
dbt-metabase - dbt + Metabase integration
dbt-expectations - Port(ish) of Great Expectations to dbt test macros
kuwala - Kuwala is the no-code data platform for BI analysts and engineers enabling you to build powerful analytics workflows. We are set out to bring state-of-the-art data engineering tools you love, such as Airbyte, dbt, or Great Expectations together in one intuitive interface built with React Flow. In addition we provide third-party data into data science models and products with a focus on geospatial data. Currently, the following data connectors are available worldwide: a) High-resolution demographics data b) Point of Interests from Open Street Map c) Google Popular Times
evidence - Business intelligence as code: build fast, interactive data visualizations in pure SQL and markdown
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
airflow-dbt - Apache Airflow integration for dbt
re_data - re_data - fix data issues before your users & CEO would discover them 😊
Scio - A Scala API for Apache Beam and Google Cloud Dataflow.
retentioneering-tools - Retentioneering: product analytics, data-driven CJM optimization, marketing analytics, web analytics, transaction analytics, graph visualization, process mining, and behavioral segmentation in Python. Predictive analytics over clickstream, AB tests, machine learning, and Markov Chain simulations.
lightdash - Self-serve BI to 10x your data team ⚡️
dbt-oracle - dbt (data build tool) adapter for Oracle Autonomous Database