Streaming
Streaming data processing is a fundamental component of modern distributed systems that need to handle continuous flows of data. Understanding the difference between real-time streaming and batch processing is crucial for designing systems that can handle both immediate responses and eventual consistency requirements.
Streaming Technologies
Apache Spark
Unified analytics engine for large-scale data processing with built-in modules for streaming, SQL, machine learning and graph processing.
Apache Flink
Framework for stateful computations over unbounded and bounded data streams, designed for true real-time processing.
Google BigQuery
Serverless, highly scalable data warehouse designed for business agility with built-in machine learning and real-time analytics.
The key distinction in streaming systems is between real-time streaming (processing data as it arrives with minimal latency) and batch processing (processing data in scheduled chunks). Both approaches have their place in modern architectures, and many systems use a combination of both to achieve optimal performance and consistency.
Real-Time vs Batch Processing
- Real-Time Streaming: Processes data continuously as it arrives, with latencies measured in milliseconds to seconds. Essential for use cases like fraud detection, live recommendations, and real-time analytics dashboards.
- Batch Processing: Processes data in large chunks at scheduled intervals (hourly, daily, etc.). Optimal for complex transformations, data reconciliation, and generating comprehensive reports.
- Hybrid Approach: Many production systems use both - real-time for immediate responses and batch for accuracy and reconciliation.
Real-Time Streaming
Real-time streaming is designed for applications that require immediate processing and response to incoming data. This approach prioritizes low latency over perfect accuracy, making it ideal for user-facing features and time-sensitive operations.
When to Use Real-Time Streaming
Perfect Use Cases:
- Fraud Detection: Credit card transactions need immediate analysis to block suspicious activity
- Live Recommendations: E-commerce sites showing "customers who bought this also bought" in real-time
- Gaming Leaderboards: Live updates of player rankings and scores
- IoT Monitoring: Immediate alerts for temperature sensors, security systems, or equipment monitoring
Key Characteristics:
- Latency: Milliseconds to seconds
- Throughput: High volume, continuous processing
- Accuracy: Eventual consistency is acceptable
- Complexity: Simpler processing logic due to latency constraints
Real-time streaming systems often sacrifice some accuracy for speed. They may process events out of order or make decisions based on incomplete information. This trade-off is acceptable when immediate response is more valuable than perfect accuracy.
Batch Processing
Batch processing handles data in large, scheduled chunks and is optimized for throughput and accuracy rather than latency. This approach allows for complex transformations and ensures data consistency across the entire dataset.
When to Use Batch Processing
Perfect Use Cases:
- Data Reconciliation: End-of-day financial reports that must be 100% accurate
- Complex Analytics: Multi-table joins and aggregations for business intelligence
- Machine Learning Training: Processing large datasets to train recommendation models
- Data Warehouse ETL: Moving and transforming data between systems
Key Characteristics:
- Latency: Minutes to hours
- Throughput: Very high for large datasets
- Accuracy: Strong consistency guarantees
- Complexity: Can handle complex transformations and multi-step processes
Batch processing is ideal when you need to ensure data accuracy and can tolerate higher latency. It's also more cost-effective for processing large volumes of data since resources can be allocated and deallocated as needed.
Lambda vs Kappa Architecture
Lambda Architecture:
- Dual Path: Combines both real-time streaming and batch processing layers
- Speed Layer: Handles real-time processing for immediate results
- Batch Layer: Processes complete datasets for accuracy and historical analysis
- Serving Layer: Merges results from both layers to provide comprehensive views
- Use Case: When you need both immediate responses and eventual accuracy
Kappa Architecture:
- Single Path: Uses only real-time streaming for all data processing
- Simplicity: Eliminates the complexity of managing two separate systems
- Reprocessing: Handles corrections by replaying events through the stream
- Use Case: When real-time processing is sufficient and batch accuracy isn't critical
- Examples: Log analytics, simple aggregations, real-time dashboards
DeepSWE Recommendation
Choosing Between Real-Time and Batch Processing:
Use Real-Time Streaming when:
- You need the fastest possible response times (fraud detection, live recommendations)
- User experience depends on immediate feedback
- Approximate results are acceptable initially
Use Batch Processing when:
- Accuracy and consistency are critical (financial reconciliation, compliance reports)
- Complex multi-step transformations are required
- Cost efficiency is important for large datasets
Best Practice - Kappa Architecture: For most modern systems, we recommend starting with a Kappa architecture that uses real-time streaming as the primary processing method. Batch processing can always serve as a reconciliatory service that runs periodically to ensure data accuracy and handle any corrections needed from the real-time layer.
This approach provides the best of both worlds: immediate responsiveness for users while maintaining data integrity through periodic batch reconciliation.