The state of Kafka replication

As organizations adopt specialized data streaming technologies, real-time data is increasingly distributed across multiple domains, clouds, and locations. With the rapid evolution of streaming-dependent technologies, companies need the ability to pivot between providers almost instantly to spread risk and maintain performance (just look at the January DeepSeek-R1 LLM news).

This creates a challenging paradox: while teams need autonomy to choose their data streaming technologies, they also need secure, compliant Kafka replication, backups, data access, and sharing across domain boundaries.

Why companies need Cross-Kafka replication

While modern solutions now offer numerous efficient ways to replicate data at rest, the replication of streaming data just hasn't evolved at the same pace. 

Why can’t we wait for our data to settle into a lake or warehouse before replicating it? Here are a few good reasons for replicating real-time data across streaming technologies such as Apache Kafka.

Data sharing across domains

Teams need their Kafka topics synchronized and available across domains for different applications and third-party consumption. This allows for collaboration and data movement across the organization and beyond. 

The rise of data marketplaces and data mesh architectures means that organizations are now treating real-time data as a product. Robust data sharing – within and outside of a business – can help to combine and monetize streaming data, to create entirely new revenue streams.

Cross-Kafka replication can be used here for several key reasons: it minimizes performance impacts from multiple external consumers, reduces latency for distant partners, isolates sensitive data from accidental exposure, and enables purpose-built clusters optimized specifically for data sharing.

Kafka migration between vendors

Lock-in is no longer acceptable. Organizations need the ability to move between vendors (like Confluent to Redpanda and back again) or to optimize costs by migrating to solutions like MSK Express Brokers. 

This is because, over the past two years, streaming infrastructure has diversified and specialized to cater for specific workloads, use cases, and industries. Teams now don’t have to make trade-offs between cost, performance, openness, and ease of use in their Kafka strategy. They can have a multi-Kafka vendor approach.

Workload migration

Efficiently move your app from one environment to another – for example, on-prem to cloud, cloud to edge, or for workload isolation.

Companies are moving data closer to where it is created, like in factories or stores – aka on the edge – while also using multiple cloud providers. This means easily moving workloads between these locations to improve performance, meet compliance requirements, and reduce latency. 

Disaster recovery

When a batch topic back-up isn’t enough, a continuous replication of data and consumer offsets comes in useful. If disaster strikes, a team can switch to the other Kafka, without waiting for a new Kafka to materialize and read from backup.

Regulations now require systems to recover within minutes and data policies to be preserved at all costs. Downtime is costly to a company's bottom line and reputation in a hyperconnected business world.

Data subsetting

When you need to copy certain production data patterns to lower tier environments. Instead of relying on synthetic data, you can mirror specific topics from production to staging while applying necessary obfuscation. Synthetic data has come a long way, but live data gives you real scenarios to troubleshoot or test against. The growing complexity of AI and ML testing environments relies on high quality, production-like data, while maintaining security and compliance. 

Masking data pipelines

You may need a two-stage Kafka architecture where sensitive data flows through a landing cluster, and is anonymized as it is exported to the main cluster. This lets you share data with third-party systems, while making sure sensitive data is properly masked before reaching downstream consumers. 

This pattern becomes more prevalent as data privacy regulations multiply, and clamp down, in parallel with the AI boom. But so does data sharing across organizational boundaries. Compliance and data movement together demand sophisticated data masking and governance models. 

The complexity behind Kafka replication

While Kafka replication might sound straightforward, the devil is in the details. You need to orchestrate multiple elements:

• Data migration: Moving messages between clusters while preserving ordering, timestamps, and headers

• Schema migration: Syncing schema registries to maintain data compatibility and evolution across clusters

• Consumer Offset migration: Transferring consumer group positions for failover and migration

• Topic configuration migration: Replicating topic-level settings like partitions, retention policies, and cleanup policies

Enterprise-grade Kafka replication requires:

• Cost-effective scaling: Optimizing resource usage while handling increasing data volumes and throughput

• Robust data governance: Enforcing data access controls, audit trails, and compliance policies across clusters

• Comprehensive Kafka monitoring: Real-time visibility into replication lag, throughput, and error rates

• Data transformation capabilities: Supporting in-flight data modifications for masking, filtering, or enrichment

• Simple, engineer-friendly Kafka configuration: Providing intuitive interfaces and declarative configuration options

• Automation support (replication-as-code): Enabling infrastructure-as-code practices for replication management.

The Kafka replication gap

With all this complexity, we need a fundamental shift in how engineers interact with distributed data, across streaming technologies. 

While current solutions allow basic data movement between Kafka clusters, they fall short of providing a unified, user-friendly operational experience across providers and deployments. 

Engineers shouldn’t have to juggle multiple tools, and interfaces, to oversee data flows between Kafka clusters, handle schema evolution, or maintain governance policies. Teams need a platform-neutral way to move and manage data across infrastructure while keeping it secure and compliant. This approach would let teams focus on getting value from their data, rather than managing the technical intricacies of cross-cluster replication. 

Organizations will continue to adopt hybrid and multi-cloud strategies. This won’t change. But the ability to work between them with ease will become a big differentiating factor – and this is where the gap exists in current Kafka replication solutions. 

The path forward

The world of data streaming is rising to meet these challenges. To do so, we're releasing Lenses K2K – addressing these needs with a vendor agnostic approach to Kafka replication.

Want to try it out? Register for the Lenses K2K preview.