Autonomous Data Onboarding Agents for Secure Real-Time Ingestion
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Solution Overview
Problem
Current data onboarding systems face challenges in efficiently ingesting and processing massive amounts of data from diverse sources in real-time, particularly in handling disparate data formats and ensuring scalable and secure data transfer across multiple domains.
Innovation Solution
A data onboarding system with a configurator that generates data onboarding objects with subscription and publication metadata, an execution engine task store, and autonomous execution agents, enabling flexible deployment and real-time data processing across various data sources and destinations, with features like encryption and load balancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is ingested from multiple diverse sources in real-time, then data processing capability is improved, but system complexity increases
Solution Approach 1:
The system segments data onboarding into independent tasks that can be executed autonomously. Each data source connection is represented as a separate onboarding task with its own execution agent, allowing parallel processing of multiple data sources without increasing overall system complexity. The execution engine divides the large-scale data ingestion problem into manageable task units.
Solution Approach 2:
The patent introduces an execution engine as an intermediary layer between diverse data sources and the data processing system. This execution engine standardizes interactions with different data sources by converting various data formats and protocols into a unified task execution model, thereby handling diversity without proportionally increasing system complexity.
2Reliability
If data is transferred across multiple domains, then data availability is improved, but security risks increase
Solution Approach 1:
The system performs preliminary actions by establishing secure connection parameters and authentication mechanisms before actual data transfer occurs. The execution engine validates data source configurations and sets up security protocols in advance, ensuring that data can be transferred across domains with pre-established security measures in place.
Solution Approach 2:
The execution engine acts as a secure intermediary that mediates data transfer across domain boundaries. It implements security controls, authentication, and data validation at the intermediary layer, protecting both the data sources and the destination system from security risks while maintaining data availability.
3Ease of operation
If autonomous execution agents are used, then operational flexibility is improved, but system complexity increases
Solution Approach 1:
The execution engine implements self-service through autonomous execution agents that automatically manage their own task execution. These agents can independently connect to data sources, retrieve data, and transfer it to the destination without requiring manual intervention for each operation. The system provides operational flexibility through automation while managing complexity by using standardized agent protocols.
4Quantity of substance
If large volumes of data are processed, then data ingestion capacity is improved, but system performance degradation occurs
Solution Approach 1:
The system segments large-volume data processing into multiple parallel execution tasks, each handled by independent execution agents. This segmentation allows the system to process large quantities of data without overwhelming a single processing thread, maintaining system performance through distributed parallel execution across multiple agents.
Data Source
AI summary
A data onboarding network includes a data onboarding configurator to generate a number of data onboarding objects, each of these data onboarding objects including subscription metadata for reading data from a data source and publication metadata for writing data to a destination data store. According to one implementation, the data onboarding system also includes an execution engine task store to store various data onboarding tasks, where the data onboarding tasks are configured to pull data from the data source using the subscription metadata and to write data to the destination data store using the publication metadata. An execution engine executes one or more of these data onboarding tasks.