Stream Processor Event-by-Event Data Handling
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Solution Overview
Problem
Conventional systems lack the capability for near real-time processing of event data from data streams, which is crucial for industries that require immediate analysis and action, such as security, financial services, and IoT, due to high event rates and rigid SQL-based processing that undermines time-sensitive data value.
Innovation Solution
A stream processor system that processes event data on an event-by-event basis, serializes data into BSON, performs operations like validation, aggregation, and timestamping, and lands processed data into sinks, with features like dead letter queues for handling invalid data and connection management through a connection registry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If SQL-based processing is used for data streams, then data processing can be performed with structured query capabilities, but near real-time processing capability is lost due to rigid processing architecture
Solution Approach 1:
The system changes the processing parameter from batch-oriented SQL operations to event-driven continuous processing, enabling near real-time speed while maintaining structured query capabilities through configurable processing pipelines
Solution Approach 2:
The processing architecture transitions from static SQL execution to dynamic event-driven processing, where the system adapts its processing behavior based on incoming event streams and configurable transformations, achieving both speed and flexibility
2Productivity
If event data is processed on an event-by-event basis, then near real-time processing is achieved, but data volume handling becomes challenging at high event rates
Solution Approach 1:
The system segments the processing workload into discrete event-handling units with configurable batch sizes, allowing efficient processing of high-volume event streams while maintaining near real-time performance through parallel processing capabilities
Solution Approach 2:
The processing engine implements universal event handling capabilities that can process various event types through a unified framework, enabling scalable handling of high event rates through configurable transformations and aggregations
3Loss of time
If data is processed prior to landing, then near real-time analytics are enabled, but data integrity requirements become more stringent
Solution Approach 1:
The system implements validation and error handling mechanisms beforehand in the processing pipeline, with dead letter queues and retry logic that cushion against data integrity issues while enabling continuous near real-time processing without data loss
Data Source
AI summary
Systems and computerized methods for processing data in a data stream prior to landing the data in a data sink is provided. The system may comprise at least one processor operatively connected to a memory, the at least one processor, when executing, being configured to receive data relating to a data source and data sink, wherein the data source is a boundless data source; establish, based on the received data relating to the data source and data sink, a connection between the data source and the data sink; receive event data from the data source; process the event data on an event-by-event basis; and land the processed event data into the data sink. By performing operations on data directly from the data stream, the system and computerized methods provided herein may provide real-time or near real-time data processing as event data is received from various data sources.