Extracting insights from metrics—and then turning those insights into action—isn’t always straightforward, especially for overworked teams. 

The latest release reduces the overhead and guesswork involved in the optimization process. Conduktor now ingests metrics from across your Kafka environment; assembles this data into trends and broader behaviors; and provides recommendations. 

Whether it’s operations, data quality, or external data sharing, these tools simplify cost management, governance, and quality control across your Kafka infrastructure. 

1. Turn Kafka metrics into actionable intelligence

From the beginning, one of Conduktor Scale’s main goals was to empower teams to move from reactive firefighting to proactive governance, via automation, improved tooling, and a unified control plane. 

Get practical suggestions from Conduktor Insights

Conduktor Insights provides actionable intelligence to make your Apache Kafka usage more efficient. Instead of drowning in data and dashboards, platform teams get tailored insights that surface the key operational issues, such as business-critical datasets or overprovisioned resources.

Best of all, this requires no additional integration. As part of Conduktor Scale, it continually monitors Kafka environments (regardless of provider), summarizes its findings, and focuses on the most common Kafka pain points, including:

• Risk analysis: Identify misconfigured topics before they cause outages or data loss

• Cost control: Pinpoint wasted cloud spend from underutilized or overprovisioned resources 

• VIP topics: Protect business-critical topics with targeted alerting and safeguards

• Governance: Track schema adoption and self-service usage across teams

Whether you’re running on-premise, in the cloud, or in hybrid deployments, Conduktor Insights provides valuable intelligence for your Kafka journey—derived from decades of experience operating large-scale Kafka environments. 

In future releases, Insights will expand to help teams optimize their specific topics and data flows, alongside the platform-wide intelligence available today. Coverage will also extend to data quality rules, business-critical topic protection, and data encryption recommendations.

The outcome: Achieve a more robust, cost-effective, and better governed streaming platform, without having to guess the next step.

Support for Confluent Cloud identity pools

Confluent Cloud Identity Pools lets platform teams group Access Control Lists (ACLs) with similar permissions, streamlining the authorization process. Conduktor now integrates with identity pools, providing more visibility into role bindings, ACLs, user actions, and which user accounts are using modern authentication protocols such as OIDC or mTLS—simplifying auditing and compliance.

The outcome: Strengthened user monitoring, investigation, and security reviews.

2. Data Quality Observability

Application developers producing data to Kafka need visibility into quality issues across their topics. Poor data quality can break downstream applications, cause latency, or impact customer-facing systems — but understanding the scope and frequency of issues is the first step.

Data quality observability, included in Conduktor Scale, extends evaluation to all topics in your Kafka environment. Developers can monitor their specific topics to understand which quality issues occur, how frequently, and whether they represent real problems or edge cases.

This visibility enables two approaches:

Proactive fixing: Identify common quality issues in the observability reports and fix them at the source—in your code, schemas, or data pipelines. Many teams resolve their data quality challenges this way without ever needing enforcement.

Informed enforcement decisions: For topics that are mature, business-critical, and have many downstream consumers, understand whether enforcement would prevent real problems or create unnecessary overhead. See what would be blocked before committing to enforcement policies.

The outcome: Understand and proactively address data quality issues, and make informed decisions about where enforcement adds value.

3. Exchange Data Sharing: Chargeback for Partner Zones

Organizations using Conduktor Exchange to share data with external partners, vendors, or customers can now track the exact usage, costs, and revenue associated with each Partner Zone.

Chargeback for Partner Zones provides near-real-time visibility into traffic generated by external partners, helping business units quantify the value of data-sharing initiatives. Teams can visualize partner consumption patterns, obtain rollups of shared data, and calculate precise ROI for individual Partner Zones.

With granular cost and revenue metrics, data product owners can identify high-value partnerships, optimize underperforming channels, and build data-driven business cases for scaling external data sharing. This simplifies budgeting, forecasting, and revenue reporting for data-as-a-product initiatives.

The outcome: Quantify the business value of external data sharing and make informed decisions about scaling data partnerships.

Conclusion

The focus of this release is to provide deeper insight into Kafka usage, operations, and costs, and from there, to enable teams to take concrete steps. Whether teams need to monitor Kafka operations or analyze revenue from Kafka data sharing, Conduktor provides the tools and knowledge to take the right action.

Ready to see it in action? 

• Current Scale customers: Conduktor Insights and Data Quality Observability are available now in your console—no additional setup required.

• Want to monetize external data sharing? Book a demo to see Chargeback for Partner Zones in action.

For a full list of what’s included in the October 2025 release, read the complete notes.