CDC Implementation Without The Bullshit

CDC implementations fail because nobody tells you the real problems. Here's what went wrong at the 3 companies where I built these systems.

The PostgreSQL 13.6 Nightmare

First company used PostgreSQL 13.6 with logical replication. Everything worked fine until we hit around 2 million events per hour. Then shit got weird.

The WAL files started growing like cancer because Debezium couldn't keep up with the replication slot. We went from 2GB WAL to 50GB in 3 hours. Production disk filled up at 2:47 AM on a Saturday.

Fixed it by tuning max_slot_wal_keep_size to 4GB and adding monitoring that pages at 80% WAL growth. But the real fix was switching from pgoutput to wal2json plugin - handles high volume way better.

Lesson: PostgreSQL logical replication will eat your disk if you don't watch WAL growth like a hawk.

Pro tip: Use this query to monitor WAL lag before it kills you:

SELECT slot_name, 
       pg_size_pretty(pg_wal_lsn_diff(pg_current_wal_lsn(), restart_lsn)) as lag_size,
       active,
       confirmed_flush_lsn
FROM pg_replication_slots;

Set up alerts when lag_size hits 1GB. At 5GB, you're in trouble. At 10GB, you're fucked.

The Kafka Networking Hell

Kafka Connect Distributed Mode Architecture: Multiple worker processes coordinate to run connectors and tasks across different availability zones. When network latency spikes between zones, connector rebalancing creates cascading failures that can take hours to recover from.

Second company had everything on AWS. Confluent Cloud looked perfect on paper. In reality, cross-AZ latency killed us.

Debezium connectors ran in us-east-1a, source DB in us-east-1b, Kafka in us-east-1c. Network latency averaged 2-3ms but spiked to 50ms during peak hours. CDC lag went from 200ms to 30 seconds randomly.

The Kafka Connect distributed mode made it worse - connectors kept rebalancing when network hiccupped. Lost 6 hours of changes during one particularly bad rebalance.

Solution: Run everything in the same AZ and fuck the high availability marketing bullshit. Deployed Kafka Connect workers on dedicated instances with local SSD storage. Cut lag to under 500ms consistently.

Lesson: Network topology matters more than the vendor demos show. Collocate your shit.

The Schema Evolution Disaster

Third company hit the schema evolution problem that nobody prepares you for. Product team added a NOT NULL column to users table without backward compatibility.

Debezium connector died instantly with org.apache.avro.AvroTypeException. But it didn't just stop - it corrupted the offset and had to restart from the beginning. 48 hours of events needed reprocessing.

Downstream applications started getting mixed schema versions. Analytics team spent 2 weeks cleaning up duplicate data. The Schema Registry compatibility checks we thought would save us? Useless when developers bypass them.

Lesson: Schema evolution will fuck you. Test every schema change in staging with actual CDC pipelines running.

Tools That Don't Suck

After 3 implementations, here's what actually works:

Debezium 3.x + PostgreSQL: Rock solid if you tune PostgreSQL properly. Use wal2json output plugin, not the default pgoutput. Monitor WAL size religiously.

AWS DMS: Good for simple MySQL -> PostgreSQL migrations. Terrible for real-time streaming. The parallel load feature breaks randomly.

Confluent Connect: Overpriced but works. Their JDBC source connector handles Oracle better than anything else. Worth the money if you have Oracle.

Airbyte: Great for batch ELT, shit for real-time CDC. The incremental sync is basically polling with lipstick.

Stop reading vendor marketing. Pick tools based on what you can actually debug when it breaks at 3 AM.

Planning for CDC? Whatever timeline you're thinking, add 6 months and double the budget. Here's what actually happens.

Month 1-2: The Honeymoon Phase

Everything looks great in development. Debezium connects to your single test PostgreSQL table, events flow to Kafka, and your analytics team is thrilled. The demo works perfectly.

Then production happens.

Month 3-4: Reality Hits

Your first production deployment breaks in 3 spectacular ways:

The Connection Pool Hell: PostgreSQL runs out of connection slots because Debezium holds connections open permanently. Your application starts throwing FATAL: sorry, too many clients already at peak traffic.

Solution: Increase max_connections from default 100 to 300, set up connection pooling, and configure Debezium's database.connectionTimeoutInMs properly. This took us 2 weeks to figure out.

The Replication Lag Spiral: CDC starts fine but lag grows from milliseconds to minutes during busy periods. WAL segments pile up, disk usage spikes, and your DBA starts panic-paging you.

Solution: Tune wal_level=logical, max_wal_senders=10, and max_replication_slots=10. Set up monitoring on pg_replication_slots and pg_stat_replication views. Nobody tells you this upfront.

Month 5-8: The Schema Change Apocalypse

Some product manager adds a column without backward compatibility testing. Your entire CDC pipeline dies with org.apache.avro.AvroTypeException. But it gets worse - the failure corrupts Kafka Connect offsets.

The Recovery Process:

1. Stop all connectors
2. Reset offsets manually: kafka-consumer-groups.sh --reset-offsets
3. Decide whether to replay 3 days of missed data or skip it
4. Deal with downstream systems that now have data gaps

This happened to us on Black Friday. Pro tip: Test schema changes in staging with actual CDC running.

Month 9-12: The Monitoring Nightmare

Your CDC works, but you have no idea when it breaks until someone complains. Building proper observability takes forever.

What You Actually Need to Monitor:

• Kafka Connect connector status (not just up/down)
• PostgreSQL replication lag: SELECT * FROM pg_replication_slots
• Kafka consumer lag per partition
• WAL disk usage and growth rate
• Schema registry availability and response time

Set up Prometheus metrics from day one. The out-of-the-box dashboards suck, so build custom Grafana dashboards.

Here's the minimal alerting setup that will save your ass:

## Prometheus alert rules
- alert: PostgreSQLWALLagHigh
  expr: pg_replication_lag_bytes > 1073741824  # 1GB
  for: 5m
  annotations:
    summary: "WAL lag is {{ $value | humanize1024 }}B"

- alert: DebeziumConnectorDown
  expr: kafka_connect_connector_status != 1
  for: 2m
  annotations:
    summary: "Connector {{ $labels.connector }} is down"

Year 2+: The Scale Wall

Everything works until traffic doubles. Then you hit limits nobody mentioned:

Debezium Single Connector Bottleneck: One connector per database means one thread handling all changes. At high volume, this becomes the chokepoint.

Workaround: Shard by table using multiple connectors with table filters. Ugly but works.

Kafka Partition Hotspots: All changes from one table go to one partition. High-volume tables create unbalanced partitions.

Solution: Configure custom partition routing based on primary key. This should have been the default.

Team Readiness Reality Check

Junior Team: Don't try CDC with junior engineers. They'll spend 6 months fighting Kafka networking instead of building features. Use Fivetran and pay the premium.

Experienced Team: Can handle Debezium if you have someone who understands Kafka operations. Budget 1 full-time engineer just for CDC operations.

Senior Team: Can build custom solutions and actually debug production issues at 3am. These teams should consider Debezium or build something custom.

The "we'll learn as we go" approach fails with CDC. Either train up first or buy managed services.

Ignore the vendor marketing. Here's what's actually happening with CDC implementations.

Tool Landscape Reality Check

Confluent is winning the enterprise sales game but not because their technology is better. They have good sales engineering and make promises executives believe. Their cloud revenue growth is impressive, but most customers end up paying 5x what they planned.

Debezium is solid if you have competent engineers. The 3.3.x release from August 2025 added better MongoDB support and TSVECTOR handling for PostgreSQL. The 3.2.x series fixed most of the PostgreSQL WAL management issues that used to kill production. But don't expect millisecond latencies - that's benchmark bullshit under perfect conditions.

AWS DMS still sucks for anything beyond basic replication. The migration success stories they advertise are simple table copies, not real-time CDC with transformations. I've never seen a complex DMS deployment that didn't require at least one "restart from scratch" moment.

GoldenGate is expensive but works. If you already have Oracle licenses and a DBA who knows it, just pay the money. Fighting with cheaper alternatives isn't worth the operational headache.

The AI Integration Hype

Most AI integration is marketing bullshit right now. I've implemented CDC for 3 companies and exactly zero used it for real-time AI features. The basic streaming analytics patterns work fine, but don't design your architecture around AI promises.

Feature stores are mostly overengineered. Simple Redis caching with CDC updates handles 90% of "real-time AI" use cases. Feast and similar tools add complexity most teams don't need.

Vector database sync is niche. Unless you're building a customer service chatbot or recommendation engine, you probably don't need Pinecone integration with your CDC pipeline.

What's Actually Happening in Production

Everyone underestimates operational complexity. The tools work, but running them reliably takes dedicated engineering time. Budget 1 full-time engineer per 100 tables under CDC, not the "set it and forget it" marketing promises.

Schema changes still break everything. The tooling has improved, but schema evolution remains the #1 cause of CDC outages. Avro schema evolution helps but doesn't eliminate the problem.

Monitoring sucks across all platforms. Out-of-the-box observability is garbage. Plan to spend 3-4 weeks building custom Grafana dashboards and Prometheus alerts that actually help debug production issues.

Regional Differences Matter

European companies are more conservative about CDC adoption due to GDPR compliance concerns. Real-time data processing creates audit trail complexity that batch ETL avoids.

Asian companies (especially in China) have better CDC adoption because they build everything from scratch anyway. Western companies struggle with legacy system integration.

US companies get pushed toward cloud-native solutions by vendor sales teams, regardless of technical fit.

Actual Deployment Patterns

Most implementations are hybrid disasters. Companies start with one CDC tool, then add others for edge cases, creating operational nightmares. Pick one approach and stick with it.

Multi-cloud CDC is mostly vendor marketing. The networking complexity and latency issues make it impractical for most use cases. Deploy CDC infrastructure in the same cloud as your source systems.

Edge computing CDC sounds cool but creates more problems than it solves. Most "edge" use cases work fine with periodic sync, not real-time CDC.

What's Coming Next (My Opinion, Not Market Research)

Consolidation around PostgreSQL logical replication as the standard CDC source. MySQL binlog is too fragile, Oracle is too expensive, and everything else is niche.

Better operational tooling because current monitoring and debugging tools are garbage. Someone will build better observability and make bank.

Simplified schema management because current schema evolution workflows are too complex for most teams to handle reliably.

Not happening: Real-time AI transformation, data mesh integration, or any other buzzword architecture patterns. Those solve problems most companies don't actually have.

Bottom Line for 2025

CDC works, but it's not magic. You'll spend 6-12 months getting it right, budget $500K+ for the first year, and need someone who can debug Kafka at 3am.

But when it works? Your data lag drops from hours to milliseconds, your engineering team stops fighting ETL schedules, and your business gets the real-time insights they've been asking for.

Just don't believe the marketing about "seamless implementation" or "zero operational overhead." That's all bullshit. Plan accordingly.