Factor Platform, Ververica integration, and kJQ enhancements

Factor Platform release candidate: Early access to unified streaming control

For organisations operating streaming at scale, the challenge has never been about any one technology. It's about managing complexity across regions, tools, and teams while maintaining governance, performance, and cost control.

We've spent years building tools that bring clarity to Apache Kafka and Apache Flink. Now, we're taking everything we've learned and building something bigger: Factor Platform, a unified control plane for real-time data infrastructure.

Factor Platform delivers complete visibility and federated control across hundreds of clusters, multiple clouds, and distributed teams from a single interface. Engineers gain deep operational insight into jobs, topics, and lineage. Business and compliance teams benefit from native catalogs, FinOps intelligence, and audit-ready transparency.

The first release candidate is live. It's designed for early adopters exploring large-scale, persistent streaming environments, and it's ready to be shaped by the teams who use it.

Interested in early access? Contact [email protected]

Unlocking native Flink management with Ververica Platform

Our collaboration with Ververica (the original creators of Apache Flink), enters a new phase with the introduction of Flex + Ververica Platform integration. This brings Flink’s enterprise management and observability capabilities directly into the Factor House ecosystem.

Flex users can now connect to Ververica Platform (Community or Enterprise v2) and instantly visualize session clusters, job deployments, and runtime performance. The current release provides a snapshot view of Ververica resources at startup, with live synchronization planned for future updates. It's a huge step toward true end-to-end streaming visibility—from data ingestion, to transformation, to delivery.

Configuration is straightforward: point to your Ververica REST API, authenticate via secure token, and your Flink environments appear right alongside your clusters.

This release represents just the beginning of our partnership with Ververica. Together, we’re exploring deeper integrations across the Flink ecosystem, including OpenShift and Amazon Managed Service for Apache Flink, to make enterprise-scale stream processing simpler and more powerful.

Read the full Ververica Platform integration guide →

Advancing Kafka support with Kafka Clients 4.1.0 and Confluent Schema SerDes 8.0.0

We’ve upgraded to Kafka Clients 4.1.0 / Confluent Schema SerDes 8.0.0, aligning Kpow with the latest Kafka ecosystem updates. Teams using custom Protobuf Serdes should review potential compatibility changes.

Read the full post →

Data Inspect gets more powerful with kJQ enhancements

Data Inspect in Kpow has been upgraded with improvements to kJQ, our lightweight JSON query language for streaming data. The new release introduces map() and select() functions, expanding the expressive power of kJQ for working with nested and dynamic data. These additions make it possible to iterate over collections, filter elements based on complex conditions, and compose advanced data quality or anomaly detection filters directly in the browser. Users can now extract specific values from arrays, filter deeply nested structures, and chain logic with built-in functions like contains, test, and is-empty.

For example, you can now write queries like:

Or filter and validate nested collections:

These updates make Data Inspect far more powerful for real-time debugging, validation, and exploratory data analysis. Explore the full range of examples and interactive demos in the kJQ documentation.

See map() and select() in action in the kJQ Playground →

Schema Registry performance improvements

We’ve greatly improved Schema Registry performance for large installations. The observation process now cuts down on the number of REST calls each schema observation makes by an order of magnitude. Kpow now defaults to SCHEMA_REGISTRY_OBSERVATION_VERSION=2, meaning all customers automatically benefit from these performance boosts.

Learn more about Schema Registry observation in the docs →