Real-time data streaming with Kafka and Flink
The shift from batch processing to real-time streaming has accelerated across industries. According to Confluent's Data Streaming Report, 75% of organizations now process data in real-time, with Apache Kafka powering the majority of these architectures. Organizations with mature streaming capabilities report 23% higher revenue growth compared to batch-only competitors.
The state of real-time streaming in 2025
According to Gartner's Data and Analytics Survey, organizations that can act on data within seconds rather than hours gain significant competitive advantages in customer experience, fraud detection, and operational efficiency.
Batch vs streaming: the paradigm shift
Batch vs Stream Processing Comparison
| Feature | Batch Processing | Stream Processing | Lambda Architecture | Kappa Architecture |
|---|---|---|---|---|
| Sub-Second Latency | ✗ | ✓ | ✓ | ✓ |
| Continuous Processing | ✗ | ✓ | ✓ | ✓ |
| Exactly-Once Semantics | ✓ | ✓ | ✗ | ✓ |
| Backpressure Handling | ✓ | ✓ | ✓ | ✓ |
| Historical Reprocessing | ✓ | ✓ | ✓ | ✓ |
| Simpler Architecture | ✓ | ✗ | ✗ | ✓ |
Kappa Architecture: Modern streaming architectures increasingly favor the Kappa pattern—treating all data as streams and reprocessing historical data through the same pipeline. This eliminates the complexity of maintaining separate batch and stream systems.
Apache Kafka fundamentals
Producers
Applications that publish events to Kafka topics
Topics
Logical channels for organizing event streams
Partitions
Physical units for parallelism and ordering
Brokers
Servers that store and serve event data
Consumers
Applications that read and process events
Consumer Groups
Coordinated consumers for parallel processing
Kafka ecosystem in 2025
Kafka Ecosystem Component Adoption (%)
Apache Flink: stream processing engine
True Stream Processing
Native streaming engine, not micro-batch. Sub-millisecond latency.
Exactly-Once Semantics
Distributed snapshots ensure no data loss or duplication.
Event Time Processing
Handle out-of-order events with watermarks and windows.
Stateful Processing
Managed state with RocksDB backend for large state.
SQL Support
Flink SQL for accessible stream processing.
Stream processing use cases
Primary Stream Processing Use Cases (2025)
Kafka + Flink architecture patterns
Source Connectors
Kafka Connect captures changes from databases, APIs
Kafka Topics
Events stored durably with configurable retention
Flink Processing
Transformations, aggregations, enrichments, joins
Output Topics
Processed data back to Kafka for consumption
Sink Connectors
Write to databases, warehouses, search engines
Applications
Dashboards, alerts, APIs consume processed data
Performance characteristics
Latency by Message Throughput (msgs/sec)
Deployment options
Kafka and Flink Deployment Options
| Feature | Confluent Cloud | Amazon MSK | Self-Managed K8s | Amazon KDA Flink |
|---|---|---|---|---|
| Managed Service | ✓ | ✓ | ✗ | ✓ |
| Kubernetes Native | ✗ | ✗ | ✓ | ✗ |
| Auto-Scaling | ✓ | ✓ | ✓ | ✓ |
| Multi-Region | ✓ | ✓ | ✓ | ✗ |
| Cost Efficiency | ✗ | ✓ | ✓ | ✓ |
| Operational Simplicity | ✓ | ✓ | ✗ | ✓ |
Schema evolution and governance
Schema Registry
Central repository for Avro, Protobuf, JSON schemas
Compatibility Checks
Enforce backward/forward compatibility rules
Version Management
Track schema versions and evolution history
Serialization
Automatic ser/de with schema references
Data Catalog
Discover and document data streams
Access Control
RBAC for topic and schema access
Schema Evolution: Schema incompatibility is the leading cause of streaming pipeline failures. Implement schema registry from day one and enforce compatibility rules in CI/CD.
Monitoring and observability
Critical Streaming Metrics Importance (%)
Common challenges and solutions
Exactly-Once Delivery
Solution: Enable idempotent producers, transactional consumers, Flink checkpointing.
Out-of-Order Events
Solution: Event time processing with watermarks, allowed lateness configuration.
Large State Management
Solution: RocksDB state backend, incremental checkpoints, state TTL.
Consumer Lag
Solution: Auto-scaling consumers, partition optimization, backpressure handling.
Data Quality
Solution: Schema validation, dead-letter queues, data quality monitoring.
Best practices
Implementation roadmap
Assess Use Cases
Identify where real-time adds value over batch
Design Architecture
Topics, partitioning, processing requirements
Start Simple
Single use case, managed service, basic monitoring
Add Complexity
Stateful processing, joins, complex event patterns
Scale Operations
Multi-cluster, DR, self-service platform
Optimize Continuously
Performance tuning, cost optimization, new use cases
FAQ
Q: When should we use Kafka Streams vs Apache Flink? A: Kafka Streams for simpler use cases embedded in applications (microservices, lightweight aggregations). Flink for complex event processing, large state, SQL interface, or when you need features like savepoints and exactly-once across systems.
Q: How do we handle schema changes in production? A: Use Schema Registry with compatibility enforcement. Prefer backward-compatible changes (adding optional fields). For breaking changes, create new topics and migrate consumers gradually.
Q: What's the right number of partitions? A: Start with throughput requirements: partitions = desired throughput / throughput per partition. Rule of thumb: 10-12 partitions for most topics, more for high-throughput topics. More partitions = more parallelism but also more overhead.
Q: How do we ensure exactly-once processing? A: Enable idempotent producers in Kafka, use transactional APIs for atomic writes, enable Flink checkpointing with exactly-once semantics, and make downstream systems idempotent.
Sources and further reading
- Confluent Data Streaming Report
- Apache Kafka Documentation
- Apache Flink Documentation
- Designing Data-Intensive Applications
- Streaming Systems by Akidau, Chernyak & Lax
Build Real-Time Systems: Implementing streaming architectures requires expertise across data engineering, distributed systems, and operations. Our team helps organizations design and build production-grade streaming platforms. Contact us to discuss your real-time data strategy.
Ready to implement real-time data streaming? Connect with our data engineering experts to develop a tailored streaming architecture.
