Streaming Migration Benefits
Supported Streaming Platforms
Apache Kafka
- Kafka Connect CDC
- Kafka Streams processing
- Schema Registry integration
- Exactly-once semantics
AWS Kinesis
- Kinesis Data Streams
- Kinesis Firehose delivery
- DynamoDB Streams CDC
- Lambda integration
Apache Pulsar
- Multi-tenancy support
- Geo-replication
- Tiered storage
- Functions framework
Change Data Capture (CDC) Approaches
Log-Based CDC (Recommended)
Capture changes directly from database transaction logs with minimal performance impact and complete change history.
Benefits:
- Near-zero performance impact
- Captures all changes (INSERT, UPDATE, DELETE)
- No schema changes required
Supported Databases:
- • MySQL (binlog)
- • PostgreSQL (logical replication)
- • Oracle (LogMiner, GoldenGate)
- • SQL Server (CDC)
- • MongoDB (oplog)
Trigger-Based CDC
Use database triggers to capture changes and write to shadow tables for streaming.
Benefits:
- Works with any database
- Customizable change capture logic
Considerations:
- • 5-10% performance overhead
- • Requires schema modifications
- • Trigger maintenance needed
Query-Based CDC
Poll database tables periodically using timestamp or version columns to identify changes.
Benefits:
- Simple to implement
- No special database permissions
Considerations:
- • Higher latency (seconds to minutes)
- • Cannot capture DELETE operations
- • Requires timestamp/version columns
4-Phase Streaming Migration
Phase 1: Assessment & Design (Days 1-3)
- Analyze streaming data sources and volumes
- Select CDC approach based on database capabilities
- Design streaming architecture and topology
- Define schema evolution and compatibility strategy
Phase 2: Infrastructure Setup (Days 4-7)
- Deploy streaming platform (Kafka/Kinesis/Pulsar)
- Configure CDC connectors and pipelines
- Set up schema registry and governance
- Implement monitoring and alerting
Phase 3: Migration & Validation (Days 8-14)
- Start CDC capture and streaming
- Validate data consistency and completeness
- Test exactly-once delivery semantics
- Verify latency and throughput SLAs
Phase 4: Cutover & Optimization (Days 15-21)
- Switch consumers to new streaming platform
- Optimize partition strategy and consumer groups
- Tune performance and resource utilization
- Decommission legacy streaming infrastructure
People Also Ask
What is the difference between CDC and event streaming?
CDC (Change Data Capture) focuses on capturing database changes and replicating them to other systems, typically for data integration and synchronization. Event streaming is broader, handling any type of event (application events, IoT data, user actions) in real-time. CDC is often implemented using event streaming platforms like Kafka. CDC provides database-level change tracking, while event streaming handles application-level events and business processes.
How do I ensure exactly-once delivery in streaming migration?
Exactly-once semantics require idempotent producers, transactional writes, and proper consumer offset management. Use Kafka transactions for atomic writes across multiple partitions. Implement idempotency keys in your data model to handle duplicate messages. Enable exactly-once semantics in Kafka Streams or use transactional APIs. For Kinesis, use sequence numbers and checkpointing. Test thoroughly with failure scenarios to verify exactly-once behavior under all conditions.
What latency can I expect with streaming data migration?
Log-based CDC typically achieves sub-second latency (100-500ms) from database commit to stream availability. End-to-end latency including processing and delivery is usually under 1 second. Factors affecting latency include network distance, batch size, processing complexity, and platform configuration. Kafka can achieve single-digit millisecond latency with proper tuning. Query-based CDC has higher latency (seconds to minutes) due to polling intervals. Real-time requirements should drive your CDC approach selection.
How do I handle schema evolution in streaming migration?
Use a schema registry (Confluent Schema Registry, AWS Glue Schema Registry) to manage schema versions and enforce compatibility rules. Implement forward and backward compatibility to allow independent producer and consumer upgrades. Use Avro, Protobuf, or JSON Schema for schema definition and evolution. Plan for additive changes (new fields) which are backward compatible. Breaking changes require coordinated upgrades or dual-write strategies. Test schema evolution scenarios before production deployment.
What are the costs of streaming data migration?
Costs include streaming platform infrastructure (Kafka clusters, Kinesis shards), data transfer (especially cross-region), storage for message retention, and compute for stream processing. Managed services like Confluent Cloud or AWS MSK simplify operations but cost more than self-managed. Typical costs: $500-5000/month for small deployments, $5000-50000/month for enterprise scale. Optimize costs through proper partition sizing, retention policies, and compression. Our AI-powered platform reduces costs by 60% through intelligent resource optimization and automated management.