Databricks. has been granted a patent for a method and system that facilitates merging target and source tables. The process involves executing jobs to identify matching rows, perform merge operations, and manage deletion vectors, ultimately producing a resulting table based on these operations. GlobalData’s report on Databricks gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Databricks, was a key innovation area identified from patents. Databricks's grant share as of June 2024 was 57%. Grant share is based on the ratio of number of grants to total number of patents.
Table merging with deletion vector management system
The patent US12045220B2 outlines a system and method for merging data from a target table and a source table, particularly when the source table contains files that differ from those in the target table. The system comprises a memory with encoded instructions and one or more processors that execute these instructions. The process begins with determining the need to merge the two tables, followed by a first job that identifies matching files between the target and source tables. This job involves storing information about these matches and the corresponding rows that align in both tables. A second job is then performed, which executes a merge operation based on the identified rows and any deletion vectors associated with previously removed rows in the target table. The outcome of this operation is a resulting table that integrates the relevant data from both sources.
Additionally, the patent details various functionalities related to managing deletion vectors, which track previously deleted rows in the target table. The system can determine and store these vectors based on metadata, ensuring that the merge operation accurately reflects the current state of the data. The instructions also allow for post-processing tasks, such as updating metadata statistics for the resulting table and ensuring the validity of these statistics. The patent emphasizes the importance of maintaining data integrity during the merging process, including the handling of files that may not be keyed to the target table. Overall, the invention provides a structured approach to data merging that enhances the efficiency and accuracy of data management systems.
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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.
