NVTabular VS daggy

Compare NVTabular vs daggy and see what are their differences.

NVTabular

NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems. (by NVIDIA-Merlin)
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NVTabular daggy
1 2
1,006 -
1.2% -
5.5 -
4 days ago -
Python
Apache License 2.0 -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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NVTabular

Posts with mentions or reviews of NVTabular. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-08.

daggy

Posts with mentions or reviews of daggy. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-08.
  • ETL Pipelines with Airflow: The Good, the Bad and the Ugly
    7 projects | news.ycombinator.com | 8 Oct 2021
    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?

When comparing NVTabular and daggy you can also consider the following projects:

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