Databricks, Inc. patent applications on 2025-07-03
Patent Applications by Databricks, Inc. on July 3rd, 2025
Databricks, Inc.: 2 patent applications
Databricks, Inc. has applied for patents in the areas of G06F16/24542 ({Plan optimisation}, 1), G06F16/254 ({Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses}, 1)
With keywords such as: data, system, transform, operation, receives, specification, processing, stream, including, represented in patent application abstracts.
Top Inventors:
- Michael Paul Armbrust of Berkeley CA US (2 patents)
- Vuk Ercegovac of Campbell CA US (2 patents)
- Paul Lappas of Seattle WA US (2 patents)
- Xi Liang of Santa Clara CA US (2 patents)
- Mukul Murthy of Berkeley CA US (2 patents)
Patent Applications by Databricks, Inc.
20250217363. COMPILE TIME PROCESSING EXTRACT, TRANSFORM, LOAD PROCESS (Databricks, .)
Abstract: a system receives etl specification for processing stream data, including a transform operation represented using a database query specification. the system generates a dataflow graph of a sequence of database queries by decomposing the database query into a first database query that generates an intermediate results table, and a second database query that receives as input the intermediate results table and outputs data used for performing the transform operation. the system executes the sequence of database queries for performing the transform operation on stream data received from the source. when receiving an incremental data set, the system determines an output change set based on the received incremental data set by traversing an execution plan and processing each operator in the execution plan, and computing a change set of a particular operator from the change sets output by the one or more other operators based on the incremental data set.
Abstract: a system receives etl specification for processing stream data, including a transform operation represented using a database query specification. the system generates a dataflow graph of a sequence of database queries by decomposing the database query into a first database query that generates an intermediate results table, and a second database query that receives as input the intermediate results table and outputs data used for performing the transform operation. the system executes the sequence of database queries for performing the transform operation on stream data received from the source. when receiving an incremental data set, the system determines an output change set based on the received incremental data set by traversing an execution plan and processing each operator in the execution plan, and computing a change set of a particular operator from the change sets output by the one or more other operators based on the incremental data set.