gNMI and Apache Hudi 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>The gNMI (gRPC Network Management Interface) Input Plugin collects telemetry data from network devices using the gNMI Subscribe method. It supports TLS for secure authentication and data transmission.</p>
<p>Writes metrics to Parquet files via Telegraf’s Parquet output plugin, preparing them for ingestion into Apache Hudi’s lakehouse architecture.</p>
Integration details
gNMI
<p>This input plugin is vendor-agnostic and can be used with any platform that supports the gNMI specification. It consumes telemetry data based on the gNMI Subscribe method, allowing for real-time monitoring of network devices.</p>
Apache Hudi
<p>This configuration leverages Telegraf’s Parquet plugin to serialize metrics into columnar Parquet files suitable for downstream ingestion by Apache Hudi. The plugin writes metrics grouped by metric name into files in a specified directory, buffering writes for efficiency and optionally rotating files on timers. It considers schema compatibility—metrics with incompatible schemas are dropped—ensuring consistency. Apache Hudi can then consume these Parquet files via tools like DeltaStreamer or Spark jobs, enabling transactional ingestion, time-travel queries, and upserts on your time series data.</p>
Configuration
gNMI
Apache Hudi
Input and output integration examples
gNMI
<ol> <li> <p><strong>Monitoring Cisco Devices</strong>: Use the gNMI plugin to collect telemetry data from Cisco IOS XR, NX-OS, or IOS XE devices for performance monitoring.</p> </li> <li> <p><strong>Real-time Network Insights</strong>: With the gNMI plugin, network administrators can gain insights into real-time metrics such as interface statistics and CPU usage.</p> </li> <li> <p><strong>Secure Data Collection</strong>: Configure the gNMI plugin with TLS settings to ensure secure communication while collecting sensitive telemetry data from devices.</p> </li> <li> <p><strong>Flexible Data Handling</strong>: Use the subscription options to customize which telemetry data you want to collect based on specific needs or requirements.</p> </li> <li> <p><strong>Error Handling</strong>: The plugin includes troubleshooting options to handle common issues like missing metric names or TLS handshake failures.</p> </li> </ol>
Apache Hudi
<ol> <li> <p><strong>Transactional Lakehouse Metrics</strong>: Buffer and write Web service metrics as Parquet files for DeltaStreamer to ingest into Hudi, enabling upserts, ACID compliance, and time-travel on historical performance data.</p> </li> <li> <p><strong>Edge Device Batch Analytics</strong>: Telegraf running on IoT gateways writes metrics to Parquet locally, where periodic Spark jobs ingest them into Hudi for long-term analytics and traceability.</p> </li> <li> <p><strong>Schema-Enforced Abnormal Metric Handling</strong>: Use Parquet plugin’s strict schema-dropping behavior to prevent malformed or unexpected metric changes. Hudi ingestion then guarantees consistent schema and data quality in downstream datasets.</p> </li> <li> <p><strong>Data Platform Integration</strong>: Store Telegraf metrics as Parquet files in an S3/ADLS landing zone. Hudi’s Spark-based ingestion pipeline then loads them into a unified, queryable lakehouse with business events and logs.</p> </li> </ol>
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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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