Real-time data streaming architecture patterns
Real-time data processing has become a competitive necessity. According to Confluent's State of Data Streaming, real-time analytics adoption grew 80% in 2024, with organizations processing millions of events per second for fraud detection, personalization, and operational intelligence.
The shift to real-time
According to Databricks State of Data Engineering, organizations with real-time capabilities see 3x faster decision-making and 40% improvement in customer experience metrics.
Streaming vs batch processing
Batch vs Stream Processing
| Feature | Batch | Streaming | Lambda | Kappa |
|---|---|---|---|---|
| Low Latency | ✗ | ✓ | ✓ | ✓ |
| High Throughput | ✓ | ✓ | ✓ | ✓ |
| Simple Reasoning | ✓ | ✗ | ✗ | ✓ |
| Cost Efficient | ✓ | ✗ | ✗ | ✓ |
| Historical Analysis | ✓ | ✗ | ✓ | ✓ |
| Real-Time Insights | ✗ | ✓ | ✓ | ✓ |
Kappa Over Lambda: The Kappa architecture (stream-only) is increasingly preferred over Lambda (batch + stream). Modern streaming systems can handle both real-time and historical replay, reducing complexity.
Streaming architecture components
Sources
Databases, APIs, IoT, clickstreams, logs
Ingestion
Kafka, Kinesis, Pulsar for durable streams
Processing
Flink, Spark Streaming, ksqlDB for transformations
Storage
Data lakes, time-series DBs, OLAP stores
Serving
APIs, dashboards, real-time features
Monitoring
Lag, throughput, error rates
Stream processing patterns
Filter & Route
Select relevant events, route to appropriate consumers. Simplest pattern.
Aggregation
Count, sum, average over time windows. Tumbling, sliding, session windows.
Join
Combine multiple streams or stream with lookup table. Complex but powerful.
Enrichment
Add context from external sources. Cache lookup data locally.
Complex Event Processing
Detect patterns across events over time. Fraud detection, anomaly detection.
Windowing strategies
Window Type Usage
Tumbling
Fixed size, non-overlapping. Count per minute.
Sliding
Fixed size, overlapping. Moving average.
Session
Gap-based. User session analytics.
Global
No time boundary. Custom triggers.
Technology comparison
Streaming Technology Adoption (%)
Stream Processing Framework Comparison
| Feature | Apache Flink | Spark Streaming | ksqlDB |
|---|---|---|---|
| Low Latency | ✓ | ✗ | ✓ |
| Exactly-Once | ✓ | ✓ | ✓ |
| Stateful Processing | ✓ | ✓ | ✓ |
| SQL Interface | ✓ | ✓ | ✓ |
| Managed Service | ✓ | ✓ | ✓ |
| Ease of Use | ✗ | ✓ | ✓ |
Handling late data
Watermarks
Track event time progress, determine when windows are complete.
Allowed Lateness
Accept late events within tolerance, update results.
Side Outputs
Route very late events to separate stream for special handling.
Reprocessing
Replay from source with corrected data.
Event Time vs Processing Time: Use event time (when event occurred) not processing time (when received) for accurate analytics. This handles out-of-order and late events correctly.
Exactly-once semantics
Delivery Semantics Tradeoffs
Operational considerations
FAQ
Q: When should we use streaming vs batch? A: Use streaming when you need results in seconds/minutes. Use batch for historical analysis, ML training, or when cost matters more than latency. Many systems use both.
Q: How do we handle state in stream processing? A: Use stateful operators with checkpointing. Flink and Kafka Streams have excellent state management. Consider state size limits and backup strategies.
Q: What about exactly-once processing? A: Modern systems (Flink, Kafka with transactions) support exactly-once semantics, but it adds overhead. Often at-least-once with idempotent writes is simpler and sufficient.
Q: How do we test streaming applications? A: Unit test transformations, use embedded Kafka/Flink for integration tests, replay production data in test environment. Testing time-based logic is particularly tricky.
Sources and further reading
- Confluent State of Data Streaming
- Streaming Systems by Akidau, Chernyak, Lax
- Kafka: The Definitive Guide
- Flink Documentation
- Designing Data-Intensive Applications
Build Real-Time Data Systems: Implementing streaming architecture requires expertise in distributed systems, data engineering, and operations. Our team helps organizations build scalable real-time data platforms. Contact us to discuss your streaming architecture needs.
Ready to implement real-time data processing? Connect with our data engineers to develop a tailored streaming strategy.
