Nightly Batch ETL Jobs→Continuous CDC Streaming Pipeline
Duration Estimate
1–3 months
Migration Stages
6
Blocking Prerequisites
2
Confidence
StrongReadiness Checklist(2 blocking, 2 advisory)
Team at senior maturity level
This migration requires senior engineering maturity. The team should have hands-on experience with the relevant technologies and proven ability to execute complex system changes safely.
Guidance: Build team skills through learning modules before attempting this migration.
Production observability baseline established
Comprehensive metrics, logs, and traces must be in place before the migration starts. You need a clear baseline to detect regressions during and after each migration stage.
Guidance: Instrument key metrics (latency p99, error rate, throughput, resource utilization) on all affected components.
Rollback plan documented and tested
Rollback feasibility for this migration is 'complex'. Rollback is possible but may be complex, document the exact rollback steps and test them in a non-production environment.
Guidance: Run a full dry-run in a staging environment that mirrors production load characteristics.
Migration trigger condition confirmed
Confirm that the actual trigger for this migration is present. Common triggers: Business requires analytics data fresher than nightly batch: inventory, orders, or user activity must be visible in dashboards within minutes; Batch ETL job runtime exceeds 4 hours and overlaps with OLTP peak load, degrading transactional query performance; Batch extraction queries cause lock contention or checkpoint pressure on the source database during the extraction window.
Guidance: Validate the trigger with production metrics or benchmarks before committing to the migration timeline.
Migration Stages
Deploy Kafka cluster. Configure Debezium connector for the source PostgreSQL database : create a replication slot, configure wal_level=logical, and start the connector. Validate that CDC events are flowing to Kafka topics: verify event format, field names, and schema representation. Monitor replication slot WAL size from day one.
Key tasks
- ·Deploy Kafka cluster. Configure Debezium connector for the source PostgreSQL database : create a replication slot, configure wal_level=logical, and start the connector. Validate that CDC events are flowing to Kafka topics: verify event format, field names, and schema representation. Monitor replication slot WAL size from day one.
- ·Validate rollback capability at this stage boundary before proceeding.
Exit criteria
- ✓'Deploy Kafka and Debezium CDC Connector' validated in production with no regression in key metrics
Rollback: possible at this stage
Implement the Flink or Spark Streaming application that transforms CDC events into the target analytics schema. Run it in parallel with the existing batch job: both pipelines write to separate destinations (or use separate target tables). Compare output for parity.
Key tasks
- ·Implement the Flink or Spark Streaming application that transforms CDC events into the target analytics schema. Run it in parallel with the existing batch job: both pipelines write to separate destinations (or use separate target tables). Compare output for parity.
- ·Validate rollback capability at this stage boundary before proceeding.
Exit criteria
- ✓'Build Stream Processing Pipeline' validated in production with no regression in key metrics
Rollback: possible at this stage
Wire the stream processor to write to the production analytics destination. Run batch and streaming outputs in parallel for the same time window. Compare row counts, aggregate values, and spot-check individual records. Identify and resolve discrepancies before any traffic is cut over.
Key tasks
- ·Wire the stream processor to write to the production analytics destination. Run batch and streaming outputs in parallel for the same time window. Compare row counts, aggregate values, and spot-check individual records. Identify and resolve discrepancies before any traffic is cut over.
- ·Validate rollback capability at this stage boundary before proceeding.
Exit criteria
- ✓'Configure Streaming Sink and Validate Data Quality' validated in production with no regression in key metrics
Rollback: possible at this stage
Run batch and streaming in parallel for 2–4 weeks. Build confidence in streaming accuracy across full business cycles (month-end, peak traffic periods, weekend load). Tune Flink watermarks and late-event handling. Validate that consumer lag stays bounded during peak source write periods.
Key tasks
- ·Run batch and streaming in parallel for 2–4 weeks. Build confidence in streaming accuracy across full business cycles (month-end, peak traffic periods, weekend load). Tune Flink watermarks and late-event handling. Validate that consumer lag stays bounded during peak source write periods.
- ·Validate rollback capability at this stage boundary before proceeding.
Exit criteria
- ✓'Extended Parallel Run' validated in production with no regression in key metrics
Rollback: possible at this stage
Switch analytics dashboards and downstream consumers to the streaming-backed data. Deprecate the batch job: keep it on standby but do not run it in production. Monitor consumer lag, checkpoint health, and data freshness SLA continuously.
Key tasks
- ·Switch analytics dashboards and downstream consumers to the streaming-backed data. Deprecate the batch job: keep it on standby but do not run it in production. Monitor consumer lag, checkpoint health, and data freshness SLA continuously.
- ·Validate rollback capability at this stage boundary before proceeding.
Exit criteria
- ✓'Analytics Cutover to Streaming' validated in production with no regression in key metrics
Rollback: possible at this stage
After 30+ days of stable streaming operation, remove the batch ETL job. Update runbooks to reflect streaming operational model: consumer lag monitoring replaces batch job execution monitoring.
Key tasks
- ·After 30+ days of stable streaming operation, remove the batch ETL job. Update runbooks to reflect streaming operational model: consumer lag monitoring replaces batch job execution monitoring.
- ·No rollback available after this stage, confirm all exit criteria before proceeding.
Exit criteria
- ✓'Decommission Batch ETL' validated in production with no regression in key metrics
Rollback: not available after this stage
Rollback Decision Points
After stage 5
Trigger: Any of the following in the 30 minutes after completing stage 5: p99 latency increases >50% from baseline, error rate exceeds 1%, or data consistency checks fail.
Procedure: Revert stage 5 changes. Rollback is complex, follow the documented rollback procedure for 'batch-to-streaming'. Engage senior engineering support.
Recovery: 1–4 hours (complex rollback)
After stage 6
Trigger: Final validation: confirm all success criteria are met within 24 hours of migration completion. If any success criterion fails, trigger rollback or remediation.
Procedure: If success criteria are not met within 24 hours, escalate to the architecture team. Decide between full rollback, partial rollback, or targeted remediation based on scope.
Recovery: 24–72 hours for full assessment and remediation
Success Criteria
- ✓Target state 'Continuous CDC Streaming Pipeline' is stable in production for 72+ hours with no regressions.
- ✓p99 latency on all affected services is within acceptable range (≤110% of pre-migration baseline).
- ✓Error rate is at or below pre-migration baseline.
This migration playbook is derived from structured architecture knowledge. Duration estimates and risk levels are structural approximations based on documented migration complexity and operational risk, not measured execution data. Validate all stages against your specific system constraints before executing.