Everyone loves to talk about Apache Kafka’s scalability, throughput, and elegant distributed architecture. Vendor marketing paints a picture of effortless real-time data streaming. Conference talks showcase impressive use cases at massive scale.

But what happens when you actually try to run Kafka in production?

Here is what we can find by analyzing thousands of posts on the Confluent Community Forum, the largest gathering place for Kafka practitioners worldwide. Beneath the success stories lies a consistent pattern of struggles that most organizations (their developers) endure.

This isn’t a critique of Kafka itself. It’s an honest look at the operational reality that rarely makes it into case studies or best practice documents. From authentication nightmares to connector failures, from configuration complexity to mysterious performance issues, real teams are wrestling with these challenges every single day.

1. Kafka Connect: The Biggest Pain Point

This appears to be the most problematic area with 831 topics:

• Configuration Complexity: Users struggle with connector setup, especially for custom transforms and sink/source configurations

• Connector Failures: Frequent issues with HTTP sink, JDBC sink, Elasticsearch sink, Lambda sink, and Debezium connectors

• Authentication Problems: Difficulty configuring SASL/SSL for internal schema history and connector authentication

• Plugin Management: Problems loading custom transforms and managing connector plugins

• Error Messages: Cryptic errors like “Unable to manage topics” and “Class not found” without clear resolution paths

• Distributed Mode: Challenges running Kafka Connect as distributed services, especially in containerized environments

2. Schema Registry Problems

181 topics with recurring issues:

• Startup Failures: “Schema Registry failed to start” with timeout exceptions

• Schema Not Found: Missing schemas causing application crashes, especially after pod restarts

• Registration Errors: 42201 errors, schema incompatibility issues

• Certificate/SSL Issues: Bad certificate errors when producers try to register schemas

• Forwarding Errors: Issues with multi-replica schema registry setups

• Integration Challenges: Difficulty connecting Schema Registry with different clients and configurations

3. Performance & Latency Issues

Critical for production systems:

• High Latency: Users reporting 5–17 second latencies in Kafka Streams applications

• Producer Performance: Intermittent 5–10 second delays in producer operations

• Consumer Lag: Persistent lag issues, especially in CDC pipelines (45+ minute lags reported)

• Foreign Key Joins: KTable foreign key joins generating millions of internal records causing performance degradation

• Throughput Drops: Sudden performance drops during broker replacements or rebalancing

4. Infrastructure & Operations Challenges

Cluster Stability:

• Broker failures after reboots due to cluster ID mismatches

• Under-replicated partitions (URPs) persistently appearing

• Split segment errors requiring manual log clearing

• New brokers not properly joining existing clusters

ZooKeeper Issues:

• Cluster ID regeneration after VM restarts

• Synchronization problems between ZooKeeper and Kafka

KRaft Migration:

• ACL configuration problems in KRaft mode

• Authentication failures during KRaft setup

• Complex migration from ZooKeeper to KRaft

5. Authentication & Security Nightmares

Extremely frustrating for users:

• SASL/SSL Configuration: Complex multi-step setup with frequent failures

• ACL Problems: “No Authorizer configured” errors in KRaft mode

• User:ANONYMOUS Issues: Unexpected anonymous user authentication attempts

• Certificate Chains: SSL handshake failures requiring certificate chain verification

• Mechanism Mismatches: SCRAM-SHA-256 not enabled when expected

• Mixed Configurations: Difficulty managing different security protocols across listeners

Pattern: Security configuration is trial-and-error with minimal helpful error messages

6. Consumer Group & Offset Management

• Frequent Rebalancing: Consumer groups rebalancing too often, causing disruptions

• Offset Reset Challenges: Unable to reset offsets for specific partitions

• Commit Failures: Offset commits failing with “group has already rebalanced” errors

• Uneven Distribution: Partitions distributed unevenly after consumer restarts

• Manual Offset Control: Complications when trying to control offset commits manually

7. Docker/Kubernetes Deployment Pain

Containerization adds complexity:

• Network Configuration: Connection refused errors within Docker Compose

• Volume Mounting: Confusion about correct directories to mount as volumes

• Resource Permissions: User ID restrictions in OpenShift

• Image Registry: Failed to fetch images from Azure Container Registry

• Cluster ID Generation: Can’t generate cluster IDs before containers start

• Storage Types: Block storage limitations with StatefulSets

• Helm Chart Confusion: Deprecated Helm charts causing confusion about migration to CFK

8. ksqlDB Query Limitations

• Pull Query Restrictions: Can’t use GROUP BY in pull queries

• Error Messages: Unclear error messages about query limitations

• Schema Compatibility: Schema incompatibility when creating multiple tables on same topic

• Windowing Requirements: Unexpected requirements for GROUP BY with windowing

• Startup Failures: Connection errors and configuration issues

9. Confluent Cloud Specific Issues

• Cost Visibility: Tags not appearing in billing CSVs or API

• Cost Forecasting: Difficulty estimating costs before implementation

• Licensing Confusion: Unclear how self-managed connector licenses work with Cloud

• Marketplace Limitations: Can’t provision through Azure Marketplace with CSP accounts

• Monitoring Integration: Challenges exporting metrics to external systems (Prometheus, ELK, CloudWatch)

• CLI Issues: Backend errors with API key creation commands

10. Monitoring & Observability Gaps

• Metrics Export: Difficulty getting metrics into Prometheus, Grafana, DataDog

• Consumer Lag: Not a direct server metric, requires special handling

• JMX Access: Questions about JMX monitoring without Docker

• Log File Locations: Confusion about where logs are stored in containers

• Alert Configuration: Under-replicated partition alerts too sensitive

11. Upgrade & Migration Complexity

• Version Compatibility: Confusion about which client versions work with which broker versions

• Direct Upgrades: Uncertainty about skipping intermediate versions

• Breaking Changes: NoClassDefFoundError after upgrading clients

• Migration Tools: MirrorMaker 2.0 not copying data properly

• Schema Versioning: Issues reverting to older schema versions

• SSL Certificate Changes: Migration breaking SSL configurations

12. Data Loss & Disaster Recovery Concerns

• Replication Issues: Topics becoming under-replicated

• Failover Complexity: Unclear what happens during cluster linking failover

• MirrorMaker Challenges: Data deletion during switchover in active-passive setups

• Persistence Concerns: Questions about guaranteeing no data loss in producer buffer

• /tmp Directory: Data loss risk when /tmp is cleared in KRaft deployments

• Backup Strategies: Unclear best practices for backups and recovery

13. Documentation & Learning Curve

• Outdated Tutorials: Commands in tutorials don’t match current documentation

• Complex Configurations: Difficulty understanding interconnected configuration parameters

• Missing Examples: Lack of complete, working examples for complex scenarios

• Lab Environment Access: New users can’t find lab environments mentioned in courses

• Non-Java Clients: Limited documentation and examples for Python, .NET, Node.js clients

• Error Interpretation: Cryptic error messages without clear resolution paths

Conclusion

The overwhelming majority of issues trace back to one root cause: configuration complexity. Whether it’s Kafka Connect, Schema Registry, security, or basic broker setup , users are drowning in interdependent parameters with minimal validation and cryptic error messages. This isn’t a few edge cases; it’s the fundamental experience for most teams.

These pain points represent a massive opportunity for the ecosystem. Organizations that can address them will capture significant value:

1. Governance platforms that provide guardrails and validation before runtime failures

2. Management tools that make complex configurations visual and testable

3. Observability solutions that explain why things are failing, not just that they failed

4. Education platforms that close the gap between documentation and reality

5. Abstraction layers that handle the complexity so teams can focus on business value

You know what? This is what conduktor.io is exactly providing.