Kafka and CrateDB Integration
Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.
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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>The CrateDB plugin facilitates the writing of metrics to a CrateDB database, leveraging its PostgreSQL-compatible protocol to ensure a seamless experience for users.</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>
CrateDB
<p>This plugin writes to CrateDB via its PostgreSQL protocol, allowing for metrics to be efficiently stored in a scalable database. CrateDB is designed for high-speed analytics, supporting time-series data and complicated queries, making it ideal for applications that require fast ingestion and analysis of large datasets. By utilizing the PostgreSQL protocol, the CrateDB output plugin ensures compatibility with existing PostgreSQL client libraries and tools, enabling a smooth integration for users who are already familiar with PostgreSQL’s ecosystem. The plugin provides options such as automatic table creation, connection parameters, and query timeouts, offering flexibility in how metrics are handled and stored within the database.</p>
Configuration
Kafka
CrateDB
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>
CrateDB
<ol> <li> <p><strong>Real-Time Analytics for IoT Devices</strong>: Collect and store metrics from thousands of IoT devices. By setting up a dynamic metrics table for each device, users can perform real-time analytics on the collected data, enabling quick insights into device performance, patterns, and potential failures. This setup benefits from CrateDB’s ability to handle high-throughput data ingestion while providing the necessary analytics capabilities to derive actionable insights.</p> </li> <li> <p><strong>Website Performance Monitoring</strong>: Track key performance metrics from web applications, such as request latency and user activity. By storing metrics in CrateDB, teams can leverage the power of SQL-like queries to analyze traffic patterns, user engagement, and server performance over time, leading to optimized application performance and enhanced user experiences.</p> </li> <li> <p><strong>Financial Transaction Analysis</strong>: Manage large volumes of financial transaction data for real-time fraud detection and analysis. With CrateDB’s scalable infrastructure, users can store, query, and analyze transaction metrics efficiently, allowing for the detection of anomalies and illicit activities based on transaction patterns and trends.</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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