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Systems and methods for building an extract, transform, load pipeline

  • US 10,685,033 B1
  • Filed: 02/14/2017
  • Issued: 06/16/2020
  • Est. Priority Date: 02/14/2017
  • Status: Active Grant
First Claim
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1. A computer-implemented method for building an extract, transform, load (ETL) pipeline, at least a portion of the method being performed by a computing device comprising at least one processor, the method comprising:

  • identifying a plurality of ETL resources employable in the ETL pipeline that are available to a user;

    categorizing each of the plurality of ETL resources based on at least one characteristic of each of the plurality of ETL resources that affects a capability of the ETL pipeline when employed in the ETL pipeline;

    provisioning the plurality of ETL resources for use with containers based on the at least one characteristic of each of the plurality of ETL resources;

    presenting a user environment to the user, the user environment including a plurality of container images corresponding to available ETL resources of the plurality of ETL resources;

    receiving, from the user, a selection of at least one container image of the plurality of container images, wherein the at least one container image includes at least one application that performs at least one action to be carried out in the ETL pipeline;

    determining two or more ETL resources from the plurality of ETL resources that are best fit to carry out the at least one action based on the at least one characteristic of each of the plurality of ETL resources; and

    running each of at least one container from the at least one container image that includes files and parameters for running the at least one container, the at least one container utilizing the two or more ETL resources of the plurality of ETL resources, wherein at least one of the two or more ETL resources comprises at least one server that hosts the at least one container, the at least one container isolating user space of the at least one container from other processes while sharing kernel space with the other processes on the at least one server.

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