Expert Guidance from the Creators of Apache Flink

Stream processing architecture decisions are expensive to reverse. Topology, state management, partitioning, and connector selection determine performance, cost, and operational complexity for years.

• Stream processing architecture design aligned to your workloads and SLAs
• Technology selection and integration patterns
• Capacity planning and resource sizing
• High availability and disaster recovery design
• Data governance and compliance architecture
• Reference architectures and implementation roadmaps

Our architects have designed stream processing systems at the largest banks, retailers, and technology companies in the world. They wrote the code that became Apache Flink.

Migrating to stream processing, or migrating between platforms, carries risk. Data continuity, application compatibility, and operational readiness all require planning and execution discipline.

Migration Paths We Execute:

• Batch-to-streaming migration for real-time analytics and operations
• Open-source Flink to Ververica Platform for enterprise governance and VERA performance
• Spark Streaming to Ververica for lower latency and true stream processing
• Kafka Streams to Ververica for complex event processing and multi-source joins
• Cloud-to-cloud migration for infrastructure consolidation
• On-premises to cloud or BYOC for operational efficiency

Migration Process

1. Assessment of current workloads, dependencies, and SLAs
2. Migration plan with risk analysis and rollback procedures
3. Application porting and testing in parallel environment
4. Cutover execution with zero-downtime procedures
5. Post-migration validation and performance benchmarking

Running stream processing and running it well are different problems. Throughput bottlenecks, state management overhead, garbage collection pauses, and serialization costs accumulate. Small inefficiencies compound at scale.

What We Optimize

• Throughput: Identify and eliminate processing bottlenecks
• Latency: Reduce end-to-end latency from source to sink
• Resource utilization: Lower CPU, memory, and storage consumption by up to 52% with VERA engine tuning
• State management: Optimize checkpoint intervals, state backends, and compaction
• Serialization: Reduce serialization overhead for high-volume workloads
• Scaling: Configure auto-scaling policies for variable load patterns

Optimization engagements typically deliver 30-50% resource reduction on existing workloads. Measured before and after. Not estimated.