Debugging time cut in half
Real-time cluster visibility and message-level search replace hours of CLI investigation with minutes.
Problem
A stack of fragmented tools, not a system.
Most enterprises running real-time data started with Kafka and now run Flink alongside it. Each technology has its own operating requirements, its own permission model, and its own audit log.
- Chasing consumer lag with CLI commands that show a single moment in time
- Routing every ACL change and access request through the one person who knows how
- Writing throwaway consumer scripts just to inspect what's in a topic
- Stitching together dashboards, logs, and manual exports to answer basic questions
- Trying to maintain consistent governance across teams without confidence that it's working
Factor Platform One control plane for
One control plane for
data streaming
Factor Platform unifies Kafka, Flink, and Iceberg, giving every team a governed, consistent view of your streaming infrastructure.
Engineers
UI
Integrations
API
Automation
CLI
Agents
MCP
Factor Platform
Unified control plane
Govern
RBAC Audit log Data masking Multi-tenancy
Inspect
Message search Schema validation Lineage Topic browser
Manage
Topic config ACLs Flink jobs Approvals
Observe
Consumer lag Cluster health Time Machine Alerting
The solution
One operational layer for your real-time data stack.
Factor Platform brings Kafka, Flink, and Iceberg into a single operational interface. You observe, debug, manage, and govern every technology in your real-time stack from one place.
Operate Kafka, Flink, and Iceberg from one interface
- Navigate from a Kafka topic to the Flink job consuming it in the same interface
- Apply the same operational workflows across Kafka and Flink
- Replace per-technology consoles with a single interface for your entire real-time stack
See your stack at any moment in time
Know exactly what your stack looked like before things went wrong. Time Machine snapshots your real-time stack at regular intervals, so when something breaks at 2am, you can navigate the entire interface back to 1:55am and replay precisely what happened.
- Troubleshooting: see what changed between healthy and unhealthy states
- Auditing: confirm what the system looked like at the time of a specific event
- Post-incident review: access the actual system state, not a reconstruction from memory
Apply one governance model across every technology
- One RBAC model, defined once and enforced across Kafka, Flink, and Iceberg
- One audit log covering every action, with timestamps and user attribution
- Multi-tenancy that scopes a team's view consistently across technologies
- Data masking that applies wherever sensitive data is being inspected
- Approval workflows that require sign-off before mutations are applied to the stack
Operate the stack with your organisation's own context
- Filter every Kafka topic, consumer group, or Flink job by the dimensions that match how your organisation is structured
- Follows the Open Lineage standard, decorating Avro schemas with business context directly
- Integrates with existing catalogs like DataHub via OpenLineage rather than building a parallel one
One UI, API, and CLI
- Multi-distribution operations: your engineers and scripts work against all Kafka distributions through the same interface
- Freedom to change providers: changing your underlying Kafka provider becomes a commercial decision, not a tooling rebuild
- Agentic operations: AI agents have a consistent API and CLI surface across Kafka and Flink
Outcomes
The operational impact of Factor Platform
Up to 90% faster event investigation
kJQ filtering and multi-topic search get engineers from question to answer without writing consumer scripts.
Fewer production incidents from bad data
Schema validation catches malformed messages before they reach your topics or crash your consumers.
Scaling to thousands of engineers
Self-service workflows mean platform teams stop being the bottleneck for routine Kafka operations.
Customers
Results from teams using Factor Platform
“We halved the time needed for debugging. When it's bad data, we're very effective at figuring out why it's bad.”
Erik Schumann
Product Owner, NORD/LB
Roles
What Factor Platform means for your role
- Stop context-switching between per-technology consoles to follow data through its lifecycle
- Your CLI and API work consistently across Kafka and Flink
- Agents you build to triage real-time data issues have a single API surface to work against
- Onboard once to one interface, not once per technology
Comparison
How Factor Platform compares
vs. Open source tooling
Running one tool per technology works at a small scale. As the stack grows, the gaps between the tools compound.
vs. Vendor-specific consoles
Vendor-specific consoles work for a single distribution of a single technology. Most enterprises do not run a single Kafka distribution.
vs. Catalog and lineage tools
Catalog tools solve a different problem. They document and trace data flows at a high level but do not operate Kafka or Flink.
vs. Building your own
Some teams build an internal portal across Kafka, Flink, & downstream technologies. The portal becomes expensive to maintain.
Kafka and Flink. Iceberg is on the near-term roadmap.
No. Factor Platform connects to your existing real-time data environment, reads metadata and metrics, and operates over each technology's wire protocol. It does not proxy your data. Nothing changes about how your Kafka clients connect to Kafka.
Yes. Factor Platform is vendor-agnostic. It works with Apache Kafka, Confluent, Amazon MSK, Aiven, Redpanda, and other Kafka-compatible streaming platforms. If you run more than one distribution, you can operate them from the same interface.
Factor Platform integrates with corporate SSO providers including Azure AD, Okta, and others. RBAC roles can be mapped to your existing identity groups.
To your environment. On-premise, your own cloud account, or air-gapped. There is no data egress to Factor House.
The same API and CLI engineers use, agents use. Because the surface is consistent across Kafka and Flink, an agent built for one technology extends to the other without reauthoring.
Pricing is based on the scope of your deployment. Book a short call with us to walk through a quote.
See what Factor Platform does for your team
Get early access and see how Factor Platform fits your Kafka and Flink environment. Or talk to us if you're evaluating at scale, we'll show you exactly how it works with your setup.