External Stream Object for Data Warehouse Integration
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Solution Overview
Problem
Existing data warehouse systems face challenges in efficiently processing queries on very large tables due to the need to scan entire tables, which consumes significant time and computing resources, and lack support for accessing external data sources via streams.
Innovation Solution
The implementation of an external stream object that facilitates transactional operations on external data stream platforms, enabling read and write operations and tracking delta changes in external data sources, allowing for native ingestion of streaming data into a data warehouse and streaming data from the warehouse to external sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If queries are executed on very large tables by scanning entire tables, then complete data retrieval is ensured, but processing time and computing resources increase significantly
Solution Approach 1:
The patent introduces an external stream object as an intermediary between the data warehouse and external data sources. This stream object enables queries to access data from external sources without requiring the data warehouse to scan entire tables, thereby reducing processing time while maintaining data retrieval completeness through the stream interface.
Solution Approach 2:
The patent segments data access by introducing stream objects that represent specific portions or views of external data sources. Instead of scanning entire tables, queries operate on segmented stream objects that provide selective access to data, reducing the computational burden while maintaining necessary data completeness.
2Measurement precision
If queries are executed on very large tables by scanning entire tables, then complete data retrieval is ensured, but computing resources are consumed significantly
Solution Approach 1:
The external stream object serves as a mediator that enables efficient data access from external sources without requiring full table scans. This intermediary mechanism reduces computing resource consumption by allowing targeted queries on stream data rather than processing entire tables through the data warehouse.
Solution Approach 2:
By segmenting data access through stream objects, the system processes only the necessary portions of external data sources rather than scanning entire tables. This segmentation approach maintains data retrieval completeness for relevant queries while significantly reducing overall computing resource consumption.
3Adaptability or versatility
If traditional database storage is used, then data can be stored and accessed, but support for external data sources and streaming operations is limited
Solution Approach 1:
The patent implements a universal external stream object that can interface with multiple types of external data sources through a standardized interface. This multi-functional stream object enables the data warehouse to work with various external sources without requiring separate specialized mechanisms for each source type, thereby enhancing adaptability while managing complexity through standardization.
Data Source
AI summary
The subject technology determines, using a connection to an external data source, a set of shards stored in an external data source, the connection to the external data source being established using an external integration, the external integration including security and configuration information. The subject technology determines a set of offsets of each shard of the set of shards. The subject technology generates a query plan indicating a degree of parallelism based at least in part on a size of the set of offsets. The subject technology, based on the set of shards and the set of offsets, performs an operation on the external data source by performing, using the connection to the external data source, a write operation from a query statement on the external data source, the external data source being different than a storage platform associated with the system.