NVTabular
daggy
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NVTabular
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ETL Pipelines with Airflow: The Good, the Bad and the Ugly
If you have GPUs, NVTabular outperforms most of the frameworks out there: https://github.com/NVIDIA/NVTabular
daggy
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ETL Pipelines with Airflow: The Good, the Bad and the Ugly
Thanks for the feedback. I'll take a look at how Luigi models task state. Right now each TaskExecutor type is responsible for running and reporting on tasks (e.g. the Slurm executor submits jobs and monitors them for completion). I was considering adding a companion "verify" stage for every vertex, which would be a command that ran and verified output. It might be a way to do what I think you're describing above without having to build in a variety of expected outputs into the daggy core. I'll check what Luigi is doing, though.
> resuming a partially failed build
Daggy does this! Right now it will continue running the DAG until every path is completed or all vertices in a processing state (queued, running, retry, error) are in the error state, then the DAG goes to an error state.
It's possible to explicitly set task/vertex states (e.g. mark it complete if the step was manually completed), then change the DAG state to QUEUED, at which point the DAG will resume execution from where it left off. [1] is a unit test that walks through that functionality.
[1] https://gitlab.com/iroddis/daggy/-/blob/master/tests/unit_se...
What are some alternatives?
dbt-expectations - Port(ish) of Great Expectations to dbt test macros
Scio - A Scala API for Apache Beam and Google Cloud Dataflow.
cuetils - CLI and library for diff, patch, and ETL operations on CUE, JSON, and Yaml
materialize - The data warehouse for operational workloads.
cascade - Lightweight and modular MLOps library targeted at small teams or individuals
federeco - implementation of federated neural collaborative filtering algorithm
powershap - A power-full Shapley feature selection method.
torchrec - Pytorch domain library for recommendation systems