Showing 4 open source projects for "parrot-security-4"

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    rudderstack

    rudderstack

    Privacy and Security focused Segment-alternative, in Golang

    Quickly deploy flexible, powerful customer data pipelines, then send the data to your entire stack—without the engineering headache. Our complete toolset makes it easy to level-up your customer data stack. Spare your data engineers the headache. Our 180+ integrations, along with custom webhook sources and destinations, save data teams hundred of hours. Say goodbye to different versions of the truth. Our SDKs track anonymous and known users at the source and reconcile users in your warehouse...
    Downloads: 1 This Week
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  • 2
    nonechucks

    nonechucks

    Deal with bad samples in your dataset dynamically

    ...Or what if your dataset is a folder full of scanned PDFs that you have to OCRize, and then run a language detector on the resulting text, because you want only the ones that are in English? Or maybe you have an AlternateIndexSampler, and you want to be able to move to dataset[6] after dataset[4] fails while attempting to load! PyTorch's data processing module expects you to rid your dataset of any unwanted or invalid samples before you feed them into its pipeline, and provides no easy way to define a "fallback policy" in case such samples are encountered during dataset iteration.
    Downloads: 0 This Week
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  • 3
    apache spark data pipeline osDQ

    apache spark data pipeline osDQ

    osDQ dedicated to create apache spark based data pipeline using JSON

    This is an offshoot project of open source data quality (osDQ) project https://sourceforge.net/projects/dataquality/ This sub project will create apache spark based data pipeline where JSON based metadata (file) will be used to run data processing , data pipeline , data quality and data preparation and data modeling features for big data. This uses java API of apache spark. It can run in local mode also. Get json example at https://github.com/arrahtech/osdq-spark How to...
    Downloads: 0 This Week
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  • 4
    Cuelake

    Cuelake

    Use SQL to build ELT pipelines on a data lakehouse

    With CueLake, you can use SQL to build ELT (Extract, Load, Transform) pipelines on a data lakehouse. You write Spark SQL statements in Zeppelin notebooks. You then schedule these notebooks using workflows (DAGs). To extract and load incremental data, you write simple select statements. CueLake executes these statements against your databases and then merges incremental data into your data lakehouse (powered by Apache Iceberg). To transform data, you write SQL statements to create...
    Downloads: 0 This Week
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