Data Ingestion Pipeline Optimizing Warehouse Utilization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data monitoring and analytics platforms face challenges in efficiently processing and analyzing vast amounts of data from cloud environments to detect anomalies and ensure security, compliance, and performance.
Innovation Solution
A data platform configured to ingest data from cloud environments, process it using data processing resources, and provide real-time monitoring and analytics through user interface resources, utilizing agents deployed on compute assets to collect and report information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is ingested into data warehouse tables without pre-filtering, then data completeness is maintained, but data warehouse performance deteriorates due to processing too much irrelevant data
Solution Approach 1:
The patent applies preliminary action by filtering and validating data before it is ingested into the data warehouse tables. The data processing system performs pre-processing operations to remove irrelevant data and validate data quality prior to loading, thereby improving warehouse performance while maintaining data completeness through targeted filtering rather than post-processing corrections.
Solution Approach 2:
The patent extracts irrelevant data from the incoming data stream before it reaches the data warehouse. By identifying and removing data that does not meet predefined criteria or is not relevant to the warehouse's analytical purposes, the system reduces the volume of data processed while preserving the completeness of relevant information.
2Productivity
If data is filtered before ingestion, then data warehouse performance improves, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing function by separating filtering and validation operations from the core data warehouse ingestion process. The data processing system operates as a distinct component that handles pre-processing tasks, while the data warehouse maintains its core analytical functions. This segmentation allows performance optimization without proportionally increasing overall system complexity.
Solution Approach 2:
The patent introduces an intermediary data processing system between the data source and the data warehouse. This intermediary layer handles the filtering and validation complexity, shielding the data warehouse from direct exposure to messy incoming data. The intermediary absorbs the processing complexity while maintaining simple, clean data ingestion into the warehouse.
3Reliability
If existing data is not cleared before loading new data, then data retention is maintained, but data warehouse accuracy deteriorates due to duplicate or outdated records
Solution Approach 1:
The patent applies preliminary action by clearing existing data from warehouse tables before loading new data, as indicated in the processor instructions. This pre-clearing operation ensures that new data is loaded into clean, empty tables, preventing duplicate records and ensuring data accuracy while maintaining the ability to retain historical data through proper data management practices.
Data Source
AI summary
Optimizing data warehouse utilization by a data ingestion pipeline, including: gathering information describing usage of a plurality of data warehouses; receiving, from a data ingestion pipeline, a request for a data warehouse to receive data from the data ingestion pipeline; selecting, based on the information, the data warehouse from the plurality of data warehouses; and providing, to the data ingestion pipeline, an indication of the data warehouse.