Kafka and Cortex Integration
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Table of Contents
Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
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Input and output integration overview
<p>This plugin allows you to gather metrics from Kafka topics in real-time, enhancing data monitoring and collection capabilities within your Telegraf setup.</p>
<p>This plugin enables Telegraf to send metrics to Cortex using the Prometheus remote write protocol, allowing seamless ingestion into Cortex’s scalable, multi-tenant time series storage.</p>
Integration details
Kafka
<p>The Kafka Telegraf plugin is designed to read data from Kafka topics and create metrics using supported input data formats. As a service input plugin, it listens continuously for incoming metrics and events, differing from standard input plugins that operate at fixed intervals. This particular plugin can utilize features from various Kafka versions and is capable of consuming messages from specified topics, applying configurations such as security credentials using SASL, and managing message processing with options for message offsets and consumer groups. The flexibility of this plugin allows it to handle a wide array of message formats and use cases, making it a valuable asset for applications relying on Kafka for data ingestion.</p>
Cortex
<p>With Telegraf’s HTTP output plugin and the <code>prometheusremotewrite</code> data format you can send metrics directly to Cortex, a horizontally scalable, long-term storage backend for Prometheus. Cortex supports multi-tenancy and accepts remote write requests using the Prometheus protobuf format. By using Telegraf as the collection agent and Remote Write as the transport mechanism, organizations can extend observability into sources not natively supported by Prometheus—such as Windows hosts, SNMP-enabled devices, or custom application metrics—while leveraging Cortex’s high-availability and long-retention capabilities.</p>
Configuration
Kafka
Cortex
Input and output integration examples
Kafka
<ol> <li> <p><strong>Real-Time Data Processing</strong>: Use the Kafka plugin to feed live data from a Kafka topic into a monitoring system. This can be particularly useful for applications that require instant feedback on performance metrics or user activity, allowing businesses to react more swiftly to changing conditions in their environments.</p> </li> <li> <p><strong>Dynamic Metrics Collection</strong>: Leverage this plugin to dynamically adjust the metrics being captured based on events occurring within Kafka. For instance, by integrating with other services, users can have the plugin reconfigure itself on-the-fly, ensuring relevant metrics are always collected according to the needs of the business or application.</p> </li> <li> <p><strong>Centralized Logging and Monitoring</strong>: Implement a centralized logging system using the Kafka Consumer Plugin to aggregate logs from multiple services into a unified monitoring dashboard. This setup can help identify issues across different services and improve overall system observability and troubleshooting capabilities.</p> </li> <li> <p><strong>Anomaly Detection System</strong>: Combine Kafka with machine learning algorithms for real-time anomaly detection. By constantly analyzing streaming data, this setup can automatically identify unusual patterns, triggering alerts and mitigating potential issues more effectively.</p> </li> </ol>
Cortex
<ol> <li> <p><strong>Unified Multi-Tenant Monitoring</strong>: Use Telegraf to collect metrics from different teams or environments and push them to Cortex with separate <code>X-Scope-OrgID</code> headers. This enables isolated data ingestion and querying per tenant, ideal for managed services and platform teams.</p> </li> <li> <p><strong>Extending Prometheus Coverage to Edge Devices</strong>: Deploy Telegraf on edge or IoT devices to collect system metrics and send them to a centralized Cortex cluster. This approach ensures consistent observability even for environments without local Prometheus scrapers.</p> </li> <li> <p><strong>Global Service Observability with Federated Tenants</strong>: Aggregate metrics from global infrastructure by configuring Telegraf agents to push data into regional Cortex clusters, each tagged with tenant identifiers. Cortex handles deduplication and centralized access across regions.</p> </li> <li> <p><strong>Custom App Telemetry Pipeline</strong>: Collect app-specific telemetry via Telegraf’s <code>exec</code> or <code>http</code> input plugins and forward it to Cortex. This allows DevOps teams to monitor app-specific KPIs in a scalable, query-efficient format while keeping metrics logically grouped by tenant or service.</p> </li> </ol>
Feedback
Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.
Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
See Ways to Get Started
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