Zendesk has filed a patent for systems and methods that balance the execution of data transformation workflows within ETL pipelines. The patent aims to promote the completion of these workflows within a specified time constraint. The method involves collecting and segregating data from multiple applications hosted by an organization, classifying providers based on selected characteristics, batching datasets, and submitting them to computing clusters for transformation. The transformed data is then made consumable by the providers. GlobalData’s report on Zendesk gives a 360-degree view of the company including its patenting strategy. Buy the report here.

According to GlobalData’s company profile on Zendesk, intelligent contact centers was a key innovation area identified from patents. Zendesk's grant share as of June 2023 was 1%. Grant share is based on the ratio of number of grants to total number of patents.

Balancing execution of data transformation workflows within etl pipelines

Source: United States Patent and Trademark Office(USPTO). Credit: Zendesk Inc

A recently filed patent (Publication Number: US20230195743A1) describes a method for balancing time-constrained data transformation workflows. The method involves executing multiple applications accessed by users within contexts corresponding to different providers. On a periodic basis, datasets associated with the providers are extracted from the applications. The providers are then classified into different classes based on predetermined criteria. The datasets of providers belonging to the same class are batched together, and these batches are submitted to a plurality of computing clusters in a balanced manner to ensure the transformation of the extracted data within a specified time constraint. Each computing cluster then transforms the batched datasets into final data for consumption by the providers.

The classification of providers is determined by calculating or estimating the amount of data in each provider's dataset and assigning them to a predetermined class that corresponds to a range of data amounts. The assigned classifications can be saved and reused in later periods, eliminating the need for repeated classification. Batching of datasets involves identifying a maximum number of datasets that can be transformed within the time constraint for each class and grouping them into one or more batches accordingly. If datasets within a batch fail to complete the transformation within the time constraint, the maximum number for that class is reduced, while if datasets within a batch repeatedly complete the transformation within the time constraint, the maximum number for that class is increased.

The patent also describes a non-transitory computer-readable medium that stores instructions for performing the method and a system for implementing the method. The system includes multiple computing devices executing the applications, a coordinator with processors and memory, and a plurality of computing clusters. The coordinator extracts datasets, classifies providers, batches datasets, and submits them to the computing clusters. The computing clusters receive the batched datasets and transform them into final data.

Overall, this patent presents a method and system for efficiently balancing time-constrained data transformation workflows by classifying providers, batching datasets, and distributing them to computing clusters in a balanced manner. The approach aims to optimize the transformation process and ensure that data is transformed within the specified time constraint.

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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.