| 1) Pure Python Nodes |
Introduce Python-only models to fal. This mirrors the dbt models for SQL. A Python model will be |
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| a script with a call to write_to_model |
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| that creates the table in the database. Python models can be referenced from other dbt models as well as python models in the project using the ref function. |
XL |
Nothing |
Done |
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| 2) Dagster Integration |
Use dagster APIs to create dynamic dagster tasks with specific resources for each fal script |
L |
Nothing |
Done |
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| 3) AWS Athena adapter |
Run all fal integration tests with AWS athena dbt adapter |
M |
Nothing |
Done |
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| 4) Sagemaker Blog Post |
Often times we want use our dbt models in ML contexts so that we can make predictions based on our data. Amazon SageMaker is an ML service that makes it easy to train ML models and to deploy them into production-level environments. In this blog we discuss a straightforward way of using your dbt models as data inputs for SageMaker. We will train a SageMaker model, use it to make some test predictions and store the results in a data warehouse. |
N/A |
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Done |
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| 5) Long Tail of Graph operators |
plus-n operator, intersection operator, “@” operator |
L |
Nothing |
Done |
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| 6) Python hooks |
Python hooks are python scripts that are invoked after each dbt model. These hooks are not invoked by graph selectors, but with the fal flow run operation command. These scripts don't create new dbt models or change the underlying table |
M |
Pure Python Nodes |
In Progress |
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| 7) Run .ipynb files as .py files |
Add ability to run notebook files as part of fal flow |
M |
Nothing |
Done |
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