Conduktor Insights Stop Firefighting. Start Predicting.

Q: How is Insights different from Prometheus/Grafana?

Monitoring tells you what's happening now. Insights tells you what's about to become a problem. We analyze patterns across topics to surface risks you'd never think to alert on - like partition skew ratios or topics with RF=1 that haven't failed yet.

Q: Do I need a large Kafka deployment to benefit?

No. Even small deployments accumulate hidden problems - empty topics, stale resources, configuration drift. Insights surfaces issues regardless of cluster size.

Q: How does Insights integrate with my existing setup?

Insights connects read-only to your Kafka clusters. No agents to install, no code changes required. Works with Confluent, AWS MSK, Redpanda, and self-managed Kafka. See the integration guide.

Your Kafka monitoring shows what's happening now. Insights shows what's about to break.

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Conduktor Insights Stop Firefighting. Start Predicting.

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The Hidden Cost: You Can't Fix What You Can't See

Most organizations discover Kafka problems during incidents. By then, the damage is done.

Silent Data Loss Risks

RF=1 with active consumers means zero redundancy on data people depend on. One broker failure, and it's gone.

Zombie Resources

100GB of storage with zero consumers. You're paying for data nobody reads.

Partition Problems

Too few partitions and you can't scale. Too many and you're wasting resources and slowing consumers.

Unknown VIPs

Which topics are business-critical? Without visibility, every topic gets the same treatment - or neglect.

Five Pillars of Kafka Intelligence

Conduktor Insights analyzes every topic across five dimensions to surface exactly where to focus.

Risk Analysis

Identify Risks Before They Become Incidents

Find topics that could cause data loss, performance degradation, or service disruptions. Insights correlates replication, volume, and consumer activity to surface risks that simple threshold alerts miss.

Low replication factor detection: Find single-replica topics instantly
Partition skew analysis: Identify uneven data distribution
Single partition bottlenecks: Spot topics that can't scale
Actionable remediation recommendations

Topics with RF=123topics at riskThese topics have no redundancy. If a single broker fails, this data is permanently lost with no way to recover.

High Partition Skew12topics unbalancedData is unevenly distributed across partitions, causing some brokers to be overloaded while others sit idle.

Single Partition + Large8scaling bottlenecksHigh-throughput topics stuck on a single partition. They can never scale beyond one broker's capacity.

Under-replicated5topics degradedThese topics are currently missing replicas. If another broker fails, you could lose data.

Cost Control

Eliminate Waste, Optimize Resources

Find topics burning storage that nobody uses. Empty topics, stale data, and tiny topics clutter your cluster and waste resources.

Empty topics: Abandoned topics cluttering your namespace and adding metadata overhead
Stale topics: No activity for configurable time periods
Tiny topics: Minimal data that could be consolidated
Resource utilization insights and cleanup recommendations

Empty Topics156abandonedTopics with zero messages that still consume metadata overhead and clutter your namespace. Safe to delete in most cases.

Stale Topics (30+ days)89inactiveNo one has produced to or consumed from these topics in over a month. Likely forgotten or deprecated.

Tiny Topics (<1MB)234consolidation candidatesVery small topics that could potentially be merged to reduce partition overhead.

Potential Savings~32%storage reductionIf you clean up empty, stale, and tiny topics, you could reclaim roughly a third of your storage.

VIP Topics

Identify High-Impact Topics Automatically

Automatically identify your most critical topics based on consumer activity, message throughput, and fanout patterns. Prioritize what matters most.

High consumer count: Topics where one failure cascades to dozens of services
Message volume analysis: Your highest-traffic data streams
Fanout detection: Topics feeding multiple services
Optimization recommendations for critical topics

orders.events47consumer groupsThis topic feeds 47 different services. If it goes down, nearly every system in your architecture is affected.

user.activity32consumer groupsA key data stream with many downstream consumers. Consider extra monitoring and higher replication.

payments.processed28consumer groupsBusiness-critical payment data. Any lag or failure here directly impacts revenue.

inventory.updates21consumer groupsWell-connected topic with healthy consumer groups and good replication settings.

Governance

Ensure Compliance and Standards

Track schema registry adoption, serialization format consistency, and self-service provisioning coverage across your organization. Surface compliance gaps before auditors do.

Schema coverage metrics: Topics with registered schemas
Serialization format distribution: Avro, JSON, Protobuf usage
Self-service coverage: Topics provisioned through proper channels
Compliance gap detection: PII without masking, retention policies that violate requirements

Schema Coverage78%of topics22% of your topics have no schema registered. Consumers have no contract and can break on any producer change.

Avro Format62%adoptionStrong typing with backward/forward compatibility. Your most governed topics use this format.

JSON Format31%adoptionJSON without a schema registry means no validation. Schema drift can silently break downstream consumers.

Self-Service Provisioned45%coverageMore than half your topics were created ad-hoc, bypassing governance. These are your shadow IT topics.

Topic Health

Actionable Health Recommendations

Get a comprehensive health score for your entire cluster and detailed recommendations for each topic that needs attention.

Cluster health score: Instant 0-100 assessment
Per-topic configuration recommendations
Retention policy optimization suggestions
Prioritized action items based on impact

Overall Health Score72/100Your cluster is functional but has significant room for improvement. Address the flagged issues to get above 85.

Topics Needing Attention48flaggedThese topics have one or more issues. They're prioritized by business impact so you know where to start.

Configuration Issues23to fixTopics with misconfigured retention, replication, or partitioning. These are causing risk or wasting resources.

Retention Optimizations15suggestedTopics where adjusting retention settings could save storage without impacting consumers.

Impact

Measurable impact from teams using Conduktor Insights.

Up to 70% unused topics discovered

Customers regularly find the majority of their topics are stale or abandoned.

Significant cost savings

Eliminate empty, stale, and tiny topics wasting storage and compute.

5 min to first insight

Instant visibility without complex setup or configuration.

100% topic visibility

Every topic analyzed across all five intelligence pillars.

Get Started with Kafka Insights

Move from reactive to predictive Kafka management. Get a personalized walkthrough of Conduktor Insights.

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Frequently Asked Questions

How is Insights different from Prometheus/Grafana?

Monitoring tells you what's happening now. Insights tells you what's about to become a problem. We analyze patterns across topics to surface risks you'd never think to alert on - like partition skew ratios or topics with RF=1 that haven't failed yet.

Do I need a large Kafka deployment to benefit?

No. Even small deployments accumulate hidden problems - empty topics, stale resources, configuration drift. Insights surfaces issues regardless of cluster size.

How does Insights integrate with my existing setup?

Insights connects read-only to your Kafka clusters. No agents to install, no code changes required. Works with Confluent, AWS MSK, Redpanda, and self-managed Kafka. See the integration guide.