Common Data Ingestion System Standardizing Heterogeneous Formats
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Solution Overview
Problem
Organizations face high costs and resource-intensive efforts in processing diverse data from various sources, necessitating an efficient system for data ingestion that standardizes data orchestration and integration across different formats and platforms.
Innovation Solution
A data ingestion system and method that includes data registration, metadata-driven processing, automated data compression, encryption, and storage lifecycle management, utilizing a data reservoir with archive, conformed, and semantic zones to standardize data formats and access, while supporting various service level agreements and delivery models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If diverse data from various sources is processed using traditional methods, then data processing capability is maintained, but processing costs and resource requirements increase
Solution Approach 1:
The patent implements a universal data ingestion system that can handle multiple data formats, sources, and destinations through a single standardized interface. The system uses common data models and standardized protocols to process diverse data types (structured, semi-structured, unstructured) without requiring separate processing pipelines for each data source, thereby reducing processing costs while maintaining adaptability.
Solution Approach 2:
The system changes the parameters of data processing by transforming various data formats into standardized internal representations. By altering the state of incoming data through normalization and standardization processes, the system enables efficient processing across different data types without increasing resource requirements proportionally to data diversity.
2Ease of operation
If data is received and processed without standardization, then data accessibility is maintained, but data orchestration complexity increases
Solution Approach 1:
The patent applies preliminary standardization actions to incoming data before processing. By pre-defining common data models, schemas, and transformation rules at the ingestion stage, the system eliminates the need for complex orchestration logic later in the data pipeline. Data is normalized and validated upfront, making subsequent operations simpler and more accessible.
Solution Approach 2:
The system introduces intermediary standardization layers between diverse data sources and processing operations. These intermediaries include standardized data models, common protocols, and normalization frameworks that mediate between heterogeneous inputs and unified processing logic, thereby reducing orchestration complexity while maintaining data accessibility.
3Productivity
If manual data processing methods are used, then processing accuracy is maintained, but processing speed and productivity decrease
Solution Approach 1:
The patent implements self-service data processing through automated validation, error detection, and correction mechanisms. The system automatically validates incoming data against defined schemas, detects anomalies, and applies corrections without manual intervention. This automation maintains processing accuracy while dramatically increasing processing speed and throughput.
Solution Approach 2:
The system incorporates feedback loops that automatically monitor processing quality, validate data transformations, and adjust processing parameters in real-time. By continuously feedback on processing accuracy metrics and automatically correcting deviations, the system maintains high reliability while operating at automated processing speeds.
Data Source
AI summary
Systems and methods for common data ingestion are disclosed. In one embodiment, in an information processing apparatus including at least a memory, a communication interface, and at least one computer processor, a method for common data ingestion may include (1) receiving a project registration for a project; (2) receiving project data associated with the project; (3) building a job associated with the project; (4) executing the job associated with the project; and (5) exporting the project data to at least one target.