Batch Vs. Stream Processing
Batch vs. stream processing represents two distinct approaches to data processing. Batch processing handles data in large, scheduled groups, while stream processing operates on data continuously in real-time as it arrives. The choice between these methods significantly impacts system architecture, latency, and resource utilization.
Understanding batch processing
Batch processing involves collecting data over time and processing it in large groups according to a scheduled trigger. This approach has been traditionally used for end-of-day (EOD) processing in financial markets, such as:
- Portfolio valuation calculations
- Risk analytics computation
- Regulatory reporting
- Settlement and clearing processes
The key advantage of batch processing is its efficiency in handling large volumes of data with predictable resource utilization. However, it introduces inherent latency since data must wait for the next batch window to be processed.
Stream processing fundamentals
Stream processing processes data immediately as it arrives, making it ideal for real-time applications in financial markets:
- Market data processing
- Real-time risk management
- Algorithmic trading
- Live market surveillance
Stream processing architectures typically use message queues or event buses to handle continuous data flows, often implementing backpressure mechanisms to manage system load.
Architectural considerations
The choice between batch and stream processing affects system design:
Performance implications
Batch processing performance
- Optimized resource utilization
- Higher throughput for large datasets
- Predictable system load
- Higher latency
Stream processing performance
- Lower latency
- Continuous resource consumption
- More complex scaling requirements
- Real-time insights
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Applications in financial markets
Batch processing use cases
- End-of-day position calculations
- Regulatory reporting
- Historical data analysis
- Back-office operations
Stream processing use cases
- Market data processing
- Real-time trading signals
- Risk monitoring
- Trade surveillance
Hybrid approaches
Modern systems often combine both paradigms:
- Stream processing for real-time analytics
- Batch processing for historical analysis
- Lambda architecture for combining real-time and batch results
- Kappa architecture for stream-first processing
Considerations for time-series data
When working with time-series data, several factors influence the choice between batch and stream processing:
- Data arrival patterns
- Latency requirements
- Analysis complexity
- Resource availability
- Cost considerations
The selection between batch and stream processing should align with:
- Business requirements
- Technical constraints
- Performance needs
- Cost considerations
- Operational capabilities
Understanding these tradeoffs helps organizations design effective data processing architectures that meet their specific needs while maintaining system performance and reliability.