Introduction

Deploying Apache Kafka in production is no small feat. While Kafka is powerful and scalable, it's also complex. Many teams rush into deployment without proper planning, leading to performance issues, data loss, and costly mistakes.

After helping hundreds of engineers deploy Kafka clusters, I've identified the most common mistakes that can derail your deployment. In this article, we'll explore these pitfalls and provide actionable solutions to avoid them.

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Mistake #1: Insufficient Infrastructure Planning

The Problem

Many teams start with a single Kafka broker or undersized infrastructure, thinking they'll "scale later." This approach leads to:

• Performance bottlenecks when traffic increases
• Data loss during broker failures
• Downtime during scaling operations
• Higher costs from emergency fixes

Real-World Impact

A fintech startup deployed Kafka with a single broker and 1GB RAM. When they reached 100K messages/second, the broker crashed, losing 2 hours of transaction data. Recovery took 8 hours and cost them $50K in lost revenue.

The Solution

Plan for Production from Day One:

1. Start with High Availability:

   • Minimum 3 Kafka brokers (tolerates 1 failure)
   • Minimum 3 Zookeeper nodes (quorum-based)
   • Replication factor of 3

2. Right-Size Your Infrastructure:

   Development:
   - Kafka Brokers: 2 vCPU, 4GB RAM (Standard_B2s)
   - Zookeeper: 1 vCPU, 1GB RAM (Standard_B1s)
   - Cost: ~$75-100/month
   
   Production:
   - Kafka Brokers: 4+ vCPU, 16GB+ RAM (Standard_D2s_v3+)
   - Zookeeper: 4 vCPU, 16GB RAM (Standard_D2s_v3)
   - Cost: ~$300-500/month
   

3. Use Infrastructure as Code:

   • Terraform for reproducible deployments
   • Version control for infrastructure changes
   • Easy scaling and environment replication

Action Items

• Use Terraform to define infrastructure
• Start with 3-broker cluster even in dev
• Size VMs based on expected throughput
• Plan for 50% capacity headroom

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Mistake #2: Ignoring Monitoring and Observability

The Problem

Many teams deploy Kafka and assume it's "working fine" without proper monitoring. They discover issues only after:

• Consumer lag reaches hours
• Brokers run out of disk space
• Network bottlenecks cause timeouts
• Replication falls behind

Real-World Impact

An e-commerce platform didn't monitor consumer lag. During Black Friday, lag reached 6 hours. Customers saw stale inventory data, leading to overselling and 500+ refund requests.

The Solution

Implement Comprehensive Monitoring:

1. Essential Metrics to Monitor:

   • Cluster Health: Active brokers, offline partitions
   • Throughput: Messages in/out per second
   • Consumer Lag: Critical for real-time systems
   • System Resources: CPU, memory, disk, network
   • Replication: Under-replicated partitions

2. Monitoring Stack:

   Prometheus → Metrics Collection
   Grafana → Visualization & Dashboards
   Alertmanager → Alert Routing
   JMX Exporter → Kafka Metrics
   Node Exporter → System Metrics
   

3. Key Dashboards:

   • Kafka Metrics Dashboard (Grafana ID: 11962)
   • Node Exporter Dashboard (Grafana ID: 1860)
   • ZooKeeper Dashboard (Grafana ID: 10465)

4. Critical Alerts:

   • Broker down
   • Consumer lag > threshold
   • Disk space < 20%
   • Under-replicated partitions > 0
   • CPU usage > 80%

Action Items

• Set up Prometheus and Grafana
• Configure JMX Exporter on all brokers
• Import standard Kafka dashboards
• Create alert rules for critical metrics
• Test alerting with broker failures

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Mistake #3: Wrong Replication and Partition Configuration

The Problem

Teams often misunderstand replication and partitions, leading to:

• Data loss when brokers fail
• Performance issues from poor partition distribution
• Inefficient scaling operations
• Consumer lag from imbalanced partitions

Common Errors

Error 1: Replication Factor of 1

# WRONG - No fault tolerance
kafka-topics.sh --create \\
  --topic my-topic \\
  --replication-factor 1  # ❌ Single point of failure

Error 2: Too Many Partitions

# WRONG - Creates overhead
kafka-topics.sh --create \\
  --topic my-topic \\
  --partitions 1000  # ❌ Too many partitions

Error 3: Too Few Partitions

# WRONG - Limits parallelism
kafka-topics.sh --create \\
  --topic my-topic \\
  --partitions 1  # ❌ Bottleneck for consumers

The Solution

Best Practices:

1. Replication Factor:
   • Development: RF=2 (tolerates 1 failure with 2 brokers)
   • Production: RF=3 (tolerates 1 failure with 3+ brokers)
   • Critical Systems: RF=3+ (tolerates multiple failures)
2. Partition Count:
   • Start Small: 3-6 partitions per topic
   • Rule of Thumb: 1 partition per consumer instance
   • Maximum: Avoid >100 partitions per topic
   • Scaling: Add partitions when needed (can't decrease)
3. Correct Configuration:

# CORRECT - Production-ready
kafka-topics.sh --create \\
  --topic my-topic \\
  --bootstrap-server broker:9092 \\
  --partitions 6 \\
  --replication-factor 3 \\
  --config min.insync.replicas=2

Action Items

• Use RF=3 for production topics
• Start with 3-6 partitions per topic
• Set min.insync.replicas=2
• Monitor partition distribution
• Plan partition count before topic creation

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Mistake #4: Neglecting Security and Access Control

The Problem

Many teams deploy Kafka without security, thinking it's "internal only." This leads to:

• Unauthorized access to sensitive data
• Data breaches from exposed clusters
• Compliance violations (GDPR, HIPAA, etc.)
• Production incidents from accidental operations

Real-World Impact

A healthcare company exposed Kafka without authentication. An intern accidentally deleted a production topic containing patient data. Recovery took 3 days and violated HIPAA compliance.

The Solution

Implement Security Best Practices:

1. Network Security:

   • Use Network Security Groups (NSG)
   • Restrict access to specific IPs
   • Use private endpoints
   • Enable VNet peering securely

2. Authentication:

   • Enable SASL/SCRAM authentication
   • Use Azure Active Directory integration
   • Implement service principals
   • Rotate credentials regularly

3. Authorization:

   • Configure ACLs for topics
   • Limit producer/consumer access
   • Separate read/write permissions
   • Audit access logs

4. Encryption:

   • Enable SSL/TLS for client connections
   • Encrypt inter-broker communication
   • Use encrypted storage
   • Encrypt data in transit

5. Basic Security Checklist:

   # Enable SSL
   listeners=SSL://broker:9093
   ssl.keystore.location=/path/to/keystore
   ssl.truststore.location=/path/to/truststore
   
   # Enable SASL
   security.inter.broker.protocol=SASL_SSL
   sasl.mechanism.inter.broker.protocol=SCRAM-SHA-512
   

Action Items

• Configure NSG rules for access control
• Enable SSL/TLS encryption
• Implement SASL authentication
• Configure topic-level ACLs
• Regular security audits

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Mistake #5: Poor Disaster Recovery and Backup Strategy

The Problem

Teams assume Kafka's replication is enough for disaster recovery. However:

• Replication doesn't protect against accidental deletion
• Regional failures can affect all replicas
• Configuration mistakes can corrupt data
• No recovery plan leads to extended downtime

Real-World Impact

A SaaS company lost all Kafka data when a developer accidentally formatted the wrong disk. They had replication but no backups. Recovery required replaying 3 months of application logs, taking 2 weeks and costing $200K.

The Solution

Implement Comprehensive Backup Strategy:

1. Multi-Region Deployment:

   • Deploy brokers across multiple Azure regions
   • Use geo-replication for critical topics
   • Test failover procedures regularly

2. Backup Strategy:

   • Topic-Level Backups: Export critical topics regularly
   • Configuration Backups: Version control all configs
   • Infrastructure Backups: Terraform state backups
   • Point-in-Time Recovery: Enable log retention

3. Disaster Recovery Plan:

   Step 1: Identify critical topics
   Step 2: Define RTO (Recovery Time Objective)
   Step 3: Define RPO (Recovery Point Objective)
   Step 4: Test recovery procedures
   Step 5: Document runbooks
   

4. Retention Policies:

   # Keep logs for 7 days
   log.retention.hours=168
   
   # Or by size
   log.retention.bytes=10737418240  # 10GB
   

5. Recovery Testing:

   • Test broker failure scenarios
   • Test data center failures
   • Test accidental deletion recovery
   • Document recovery times

Action Items

• Define backup strategy for critical topics
• Implement retention policies
• Test disaster recovery procedures
• Document recovery runbooks
• Regular backup verification

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Summary: The 5 Mistakes Checklist

Use this checklist to avoid common Kafka deployment mistakes:

Infrastructure

• Deploy 3+ brokers for high availability
• Right-size VMs for expected load
• Use Infrastructure as Code (Terraform)
• Plan for 50% capacity headroom

Monitoring

• Set up Prometheus and Grafana
• Configure JMX Exporter
• Import standard dashboards
• Create critical alerts
• Test alerting

Configuration

• Use RF=3 for production
• Start with 3-6 partitions
• Set min.insync.replicas=2
• Monitor partition distribution

Security

• Configure NSG rules
• Enable SSL/TLS
• Implement authentication
• Configure ACLs
• Regular security audits

Disaster Recovery

• Define backup strategy
• Set retention policies
• Test recovery procedures
• Document runbooks

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Learn from Mistakes: Master Kafka Deployment

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• ✅ Replication and partition best practices
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• ✅ Disaster recovery and backup strategies
• ✅ Real-world production scenarios

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