Change Data Capture - Stream Database Changes So Your Data Isn't 6 Hours Behind

I've implemented CDC at three companies. Here's what actually happens and why you'll end up doing it too.

The Problem Everyone Hits

Your data team starts with nightly ETL jobs. Works great until:

• Business wants "real-time" dashboards (they mean 5-minute refresh, you know it means hours of debugging)
• Someone changes a database column and your entire pipeline dies at 3AM
• Users complain data is "stale" when it's only 6 hours behind
• You need to sync data between 5 different systems and each sync takes longer

How CDC Actually Works

Your database already logs every change to its transaction log - PostgreSQL calls it WAL, MySQL calls it binlog. CDC just taps into that stream and says "hey, this row changed, here's what happened." No queries hammering your production tables, no full table scans at 3am.

Three ways to actually implement this stuff:

Log-based CDC - The good shit. Read transaction logs directly. Works great if your database isn't ancient.

Trigger-based CDC - Database triggers fire on every change. Sure, it works everywhere, but watching your production queries slow to a crawl isn't fun.

Query-based CDC - Just poll for changes using timestamps. Simple as hell, but you'll miss deletes and it's not really real-time.

When CDC Actually Helps

CDC shines when:

• You have high-change tables that need to sync quickly
• Downstream systems can't wait for batch runs
• You need to replicate deletes (triggers and polling struggle with this)
• Source system can't handle heavy query load from ETL

When it doesn't help:

• Low-change tables (less than 1000 changes/day)
• Complex transformations (do those downstream)
• Compliance requires batch processing
• Legacy databases with shitty log access

The Real Implementation Pain Points

WAL Retention Hell: PostgreSQL WAL files will fill your disk if CDC falls behind. Set max_slot_wal_keep_size or you'll run out of space. I watched Ubuntu systems shit the bed when /var/lib/postgresql/data hits 95% - server just stops responding and you're SSH'ing in at 2am to clean up WAL files. This Stack Overflow post shows the exact problem that made me lose a weekend.

MySQL Binlog Position Tracking: Lose track of the binlog position and you're either missing data or reprocessing everything. Debezium MySQL connector docs explains position tracking but good luck finding the relevant section.

Schema Evolution: Adding a column is fine. Renaming or dropping columns will break your CDC pipeline in exciting ways. Debezium pain points blog covers what actually breaks in production.

Network Partitions: When your CDC process can't reach Kafka for 30 minutes, fun things happen to your lag metrics. Kafka Connect troubleshooting has the monitoring queries you'll need.

Those comparison tables look clean, but here's what actually happens when you implement CDC in production.

What "Minimal Performance Impact" Actually Means

The marketing says log-based CDC adds "1-3% overhead." In my experience:

• PostgreSQL: WAL overhead is real but manageable if you size disk correctly
• MySQL: Binlog I/O can spike during high-write periods
• SQL Server: CDC can impact tempdb if you don't tune properly

You need monitoring. PostgreSQL CDC monitoring queries help track WAL generation rate and slot lag.

The Schema Change Minefield

"Automatic schema evolution" is mostly bullshit. Here's what actually works:

Adding columns: Usually fine, new columns appear as null
Renaming columns: Breaks everything. Plan downtime.
Changing data types: VARCHAR(50) to VARCHAR(100) works, INT to VARCHAR does not
Dropping columns: Some tools handle this, others shit the bed

We learned to test schema changes on dev environments first. Revolutionary concept, I know.

Debugging CDC When It Breaks (And It Will Break)

Debezium Memory Issues: Debezium 1.9.x has memory leaks with large transactions. I learned this the hard way when our connector died during a 2M row batch update at 4am on a Saturday - took 3 hours to figure out it was a known issue. Upgrade to 2.x or restart connectors weekly. Don't be me.

Kafka Connect Restarts: Kafka Connect dies randomly. Set connect.log.level=DEBUG and prepare for log diving.

WAL/Binlog Position Loss: This is your worst nightmare. Lost position means reprocessing everything or missing data. Took down prod for 2 hours when someone deleted our Kafka offsets topic. Debezium stores offsets in Kafka - monitor those topics like your life depends on it.

Lag Monitoring: Set up alerts on replication lag. When lag hits 10+ minutes, someone's phone should ring.

Tool Selection Reality

Debezium: Free but you'll spend 6 months learning it. Official documentation is scattered across 47 different pages. Debezium Slack community is where you'll actually get help.

Airbyte: Easier setup, costs money, connectors restart mysteriously. Your ops team will hate the random failures but love the UI.

AWS DMS: Slow as hell but it works. DMS CDC setup guide has everything you need. Your ops team already knows how to fix it when it breaks.

Fivetran: Expensive but actually works reliably. You pay for the pain you don't experience.

Cost Reality Check

"Open-source Debezium is free!" Sure, if you ignore:

• Kafka cluster hosting ($2-5k/month depending on throughput)
• Engineer time (20% of someone's job maintaining it, more during incidents)
• Monitoring and alerting setup (because you WILL get paged at 3am)
• The therapy costs from debugging Kafka Connect failures

Budget $50-100k/year for a production CDC setup including people, infrastructure, and your sanity.

When You Should Just Use Batch ETL

Don't let CDC become a golden hammer:

• Tables with fewer than 10k changes/day - batch ETL is simpler
• Heavy transformations - do them downstream, not in CDC
• Compliance requirements that mandate batch processing
• When your team doesn't have streaming expertise yet

Start simple, add complexity when you actually need it.