Stop Using Kafka Connect for Backups. You Are Building a Trap, Not a Safety Net

Why an integration tool will let you down when it matters most, and what actual disaster recovery looks like.

‍

Let’s be honest: Your "Backup" is just a data dump.

You set up Kafka Connect (an S3 Sink Connector, maybe a GCS Sink) and now your topics are flowing into object storage. The compliance checkboxes are ticked. It feels like you have backup fully covered.

‍

You don’t. You have an integration pipeline, and a leaky one at that.

There's a big difference between moving data out of Kafka and being able to restore a running cluster after something goes wrong. And when something does go wrong (ransomware, a region outage, a developer who deleted the wrong topic at 11pm on a Friday) Kafka Connect won't save you. Depending on how much you've leaned on it, it might actually be part of the problem.

Here is why relying on Connect for Disaster Recovery (DR) is a dangerous illusion.

‍

1. Restoring from Connect is a manual engineering job

Kafka Connect moves data from A to B. That's what it's designed for, and it does it well. But restoring from it means reversing that flow under pressure, and that's a different challenge entirely.

The S3 Sink Connector writes data in a format optimized for analytics, partitioned by time or field. To restore, you need to configure a Source Connector from scratch. That means manually mapping topic names, handling partition mismatches, and dealing with message ordering if the files weren't written sequentially. 

In a live incident, you don't have time to engineer a reverse pipeline. You need a restore button.

‍

2. The Schema Registry problem nobody talks about

This is the hidden killer. When you dump Avro, Protobuf, or JSON Schema data via Kafka Connect, you're often leaving the Schema Registry context behind.

Restore that data to a new cluster (which is exactly what DR scenarios require) and the Schema IDs won't match. The new registry assigns its own IDs. Your consumers expect the old ones. Deserialization fails.

With Connect, you end up writing custom scripts to patch schema IDs while your production systems are down. Kannika Armory handles Schema ID mapping automatically, so the restored data works with your new registry out of the box.

‍

3. The cost trap: death by a thousand PUTs

Kafka Connect's S3 Sink forces you into a bad choice: low data loss or low cloud costs. You can't have both.

To keep your Recovery Point Objective (RPO) tight, you need frequent flushes. That means Kafka Connect writes tons of tiny files to S3, and each one triggers a PUT request. Those PUTs add up fast. In many cases, the PUT charges end up costing more than the actual storage.

The fix would be streaming appends, where you mimic Kafka's native log structure directly on object storage and skip the constant file rotation overhead. Standard Kafka Connect doesn't support that. You're stuck choosing between an expensive transaction tax or a backup that's dangerously out of date.

‍

4. The silent killer: topic recreation

Here's a problem most teams don't see coming until it's too late.

If you recreate a topic (new topic, same name), offsets reset to zero. The Sink Connector doesn't know the difference. It starts writing new data that either overwrites your old backups or creates conflicting duplicate offsets across different time partitions. The result is a poisoned backup. The data is technically there, but you can't restore it in any logical way.

‍

5. Managing backups shouldn't require a terminal window

When disaster strikes, you need instant clarity, not a stack of JSON config files and a REST API.

Kafka Connect turns backup management into a black box. Checking on a failed backup means digging through worker logs or hitting an API endpoint with curl. That's time you don't have during an outage.

You can manage Connect via Infrastructure as Code, but it's far from smooth. Connectors maintain their own runtime state, so integrating them into a declarative pipeline like Terraform often leads to drift, restart loops, or silent failures. It works, but it's brittle.

Kannika Armory solves both problems. It has a dedicated GUI for immediate visual status checks and one-click restores. It also provides a Kubernetes-native operator, so you can define your backups as YAML and let the system handle the lifecycle automatically. UI for speed, GitOps for control.

‍

6. Your backups should do more than sit there

A backup that's only useful in a disaster is a missed opportunity.

Restoring production data to a staging environment with Kafka Connect is a manual, error-prone process, assuming you can pull it off at all. Kannika Armory supports environment cloning.

Got a production issue you need to reproduce? Spin up a staging cluster, clone the relevant topics with correct schema mappings, and you're working with real data in minutes. No scripts. No shortcuts. No risk to production.

‍

The verdict: Use the right tool for the job

Kafka Connect is great for feeding your data lake. It's not built for saving your business when things go sideways.

Disaster recovery is binary: it works or it doesn't. Kannika Armory was built to make sure it works. You pick a timestamp, you hit restore, and you're back.

Don't wait for a production incident to find out your "backup" is just a folder full of JSON files.

‍

Ready to see what actual Kafka DR looks like? Talk to our Kafka experts