In today’s digital landscape, enterprises are increasingly adopting multi-cloud strategies to reduce vendor lock-in, improve resilience, and comply with regulatory requirements. Apache Kafka, as a distributed event streaming platform, is well-suited to power cross-cloud data pipelines — but doing so introduces new challenges around latency, replication, and networking.

This post explores how to run Kafka in multi-cloud environments, and highlights strategies to manage cross-cloud data streams efficiently and securely.

Why Multi-Cloud Kafka?

Use cases driving Kafka across clouds:

  • Geographic redundancy for disaster recovery
  • Regulatory boundaries requiring local data residency
  • Cloud cost optimization by distributing workloads
  • Interoperability between SaaS applications and private cloud workloads

Running Kafka across clouds requires architectural planning to preserve low latency, message durability, and availability guarantees.

Architecture Patterns

1. Active-Passive Cluster (Disaster Recovery)

One Kafka cluster is active (primary), the other is passive (standby). Data is replicated from active to passive.

[Cloud A Kafka Cluster] → [Cloud B Kafka Cluster (DR)]
↑                         ↑
Producers                 MirrorMaker 2
↓                         ↓
Consumers                 Cold Standby

Best for: DR/Failover setups with manual or automated failover.

2. Active-Active Cluster (Bi-Directional Replication)

Both Kafka clusters ingest and serve traffic. Topics are replicated in both directions.

[Cloud A] ⇄ MirrorMaker 2 ⇄ [Cloud B]
↑                                 ↑
Producers A                     Producers B
Consumers A                     Consumers B

Best for: Global multi-region apps with localized access, but needs conflict resolution strategies.

3. Central Kafka, Edge Producers/Consumers

Kafka runs in one cloud, but producers or consumers span multiple clouds or data centers.

[Cloud A Kafka Cluster]
↑        ↑        ↑
Cloud A   Cloud B   On-Prem
Producer  Consumer  Ingestor

Use VPC peering or VPN to connect securely. This avoids managing replication but introduces latency risks.

Cross-Cloud Replication with MirrorMaker 2

Kafka’s MirrorMaker 2 (MM2) allows topic replication between clusters using Kafka Connect.

Example config:

clusters = A, B

A.bootstrap.servers = kafka-a.cloud.com:9092
B.bootstrap.servers = kafka-b.cloud.com:9092

A->B.enabled = true
A->B.topics = .*
A->B.emit.heartbeats.enabled = true

MM2 ensures:

  • Message ordering
  • Checkpoints
  • Metadata sync

Alternatives include:

  • Confluent Replicator
  • Datastream (GCP) + Kafka Connect
  • AWS MSK Connect with custom connectors

Security Considerations

  • Use TLS encryption for broker-to-broker and client communication
  • Secure credentials with SASL or OAuth
  • Use firewalls, VPC peering, or VPN tunnels
  • Monitor data egress costs between clouds

Monitoring and Observability

Key metrics to watch:

  • Replication lag (offsets behind)
  • Consumer lag across clouds
  • Throughput and errors in MirrorMaker connectors
  • Cluster health via JMX or Prometheus exporters

Tools:

  • Prometheus + Grafana
  • Confluent Control Center
  • Datadog, New Relic, or Cloud-native monitors

Challenges and Solutions

Challenge Solution
High latency Place producers close to Kafka cluster
Out-of-order replication Use partitioned topics and MM2 checkpoints
Conflict in active-active Use append-only topics or key-based routing
Network cost Use data compression + minimized replication scope

Best Practices

  • Always encrypt cross-cloud traffic (TLS)
  • Use dedicated topics per cloud for clarity
  • Design topics with partition keys aligned to geography or tenant
  • Enable auto-throttling and retries in producers
  • Consider cloud-native alternatives (e.g., EventBridge, Pub/Sub) for edge cases

Conclusion

Kafka can effectively power multi-cloud data pipelines, provided you plan for replication, latency, and security challenges. Whether you’re building for resilience, regulatory boundaries, or multi-region scalability, Kafka offers the tools and flexibility to stream data securely and efficiently across clouds.

With the right design patterns — and tools like MirrorMaker 2 and Kafka Connect — you can bridge data and applications across AWS, GCP, Azure, and on-prem environments with confidence.