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Kafka
Management

Installation, topic design, replication, backup, and failure recovery for production Apache Kafka — sized correctly, tested for real failures, and supported end to end.

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Complete Kafka Management

From first broker to 2 AM failure recovery — everything a production Kafka cluster needs.

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Installation & Cluster Setup

A correctly sized, correctly configured cluster from the first broker.

  • Multi-broker cluster deployment (KRaft or ZooKeeper)
  • Broker hardware/resource sizing
  • Cluster networking & listener configuration
  • Schema Registry deployment (Avro/Protobuf)
  • TLS & SASL authentication setup
🗂️

Topic & Partition Management

Topic design that fits your throughput and consumer parallelism, not guesswork.

  • Partition count & key strategy design
  • Retention & compaction policy configuration
  • Topic naming standards & governance
  • Consumer group design & rebalancing strategy
  • Kafka Connect source/sink configuration
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Replication & High Availability

Configured to survive a broker failure without losing committed messages.

  • Replication factor & in-sync replica (ISR) tuning
  • Rack/AZ-aware replica placement
  • Min in-sync replicas for durability guarantees
  • Broker failure & leader election testing
  • Rolling broker upgrades with zero downtime
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Backup & Disaster Recovery

Cross-cluster replication and configuration backup, not just hoping the cluster never fails.

  • MirrorMaker 2 cross-region/cross-cluster replication
  • Cluster metadata & configuration backup
  • Topic/ACL/schema backup automation
  • DR failover runbook documentation
  • Regular DR failover drills
📊

Monitoring & Performance Tuning

Consumer lag and broker health visible before they become an outage.

  • Consumer lag & throughput dashboards
  • Broker JVM, disk & network monitoring
  • Prometheus/JMX exporter deployment
  • Producer/consumer throughput tuning
  • Under-replicated partition alerting
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Debug & Failure Recovery

Real Kafka expertise when a broker won't rejoin or a consumer group stalls.

  • Broker failure & under-replication recovery
  • Consumer group stuck/rebalance troubleshooting
  • Data loss investigation & ISR analysis
  • Disk full / log segment cleanup
  • Post-incident runbook documentation
📨

Sized for Your Load, Tested for Real Failures

Kafka's durability guarantees only hold if replication is configured correctly and actually tested. We don't take that on faith.

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ISR Configured Deliberately

Replication factor and min in-sync replicas are set based on your actual durability requirements, not left at defaults that silently under-protect critical topics.

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Broker Failure Tested Before Go-Live

We kill a broker on purpose during onboarding and confirm leader election and consumer failover actually work — not just assume the replication config is correct.

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Consumer Lag as an Early Warning

A growing consumer lag almost always predicts trouble before it becomes visible to end users. We alert on the trend, not just a threshold.

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Real Kafka Expertise on Call

Under-replicated partitions, stuck consumer groups, disk-full brokers — we diagnose the actual root cause, not just bounce the broker and hope.

Platforms & Tools We Work With

📨
Apache Kafka
Event Streaming
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Schema Registry
Avro / Protobuf
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Kafka Connect
Source/Sink Integration
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MirrorMaker 2
Cross-Cluster Replication
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Kafka UI / AKHQ
Admin Console
📊
Prometheus / Grafana
Monitoring
🔐
SASL / SSL
Authentication & Encryption
🌀
Kafka Streams / ksqlDB
Stream Processing

How We Onboard & Support

01

Workload Assessment

Message volume, retention needs, and consumer patterns reviewed to size the cluster correctly.

02

Cluster Build

Brokers deployed, topics designed, replication and security configured for your workload.

03

Test Failure Scenarios

Broker-kill and under-replication tests run before go-live to confirm durability actually holds.

04

Operate & Support

24×7 lag/throughput monitoring, upgrades, and on-call failure recovery — ongoing.

Common Questions

Ready for Kafka
Support You Can Rely On?

Our engineers will audit your Kafka cluster and identify quick wins on durability and cost.