gNMI and Databricks Integration
Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.
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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.
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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>Use Telegraf’s HTTP output plugin to push metrics straight into a Databricks Lakehouse by calling the SQL Statement Execution API with a JSON-wrapped INSERT or volume PUT command.</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>
Databricks
<p>This configuration turns Telegraf into a lightweight ingestion agent for the Databricks Lakehouse. It leverages the Databricks SQL Statement Execution API 2.0, which accepts authenticated POST requests containing a JSON payload with a <code>statement</code> field. Each Telegraf flush dynamically renders a SQL INSERT (or, for file-based workflows, a <code>PUT ... INTO /Volumes/...</code> command) that lands the metrics into a Unity Catalog table or volume governed by Lakehouse security. Under the hood Databricks stores successful inserts as Delta Lake transactions, enabling ACID guarantees, time-travel, and scalable analytics. Operators can point the <code>warehouse_id</code> at any serverless or classic SQL warehouse, and all authentication is handled with a PAT or service-principal token—no agents or JDBC drivers required. Because Telegraf’s HTTP output supports custom headers, batching, TLS, and proxy settings, the same pattern scales from edge IoT gateways to container sidecars, consolidating infrastructure telemetry, application logs, or business KPIs directly into the Lakehouse for BI, ML, and Lakehouse Monitoring. Unity Catalog volumes provide a governed staging layer when file uploads and <code>COPY INTO</code> are preferred, and the approach aligns with Databricks’ recommended ingestion practices for partners and ISVs.</p>
Configuration
gNMI
Databricks
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>
Databricks
<ol> <li><strong>Edge-to-Lakehouse Telemetry Pipe</strong>: Deploy Telegraf on factory PLCs to sample vibration metrics and post them every second to a serverless SQL warehouse. Delta tables power PowerBI dashboards that alert engineers when thresholds drift.</li> <li><strong>Blue-Green CI/CD Rollout Metrics</strong>: Attach a Telegraf sidecar to each Kubernetes canary pod; it inserts container stats into a Unity Catalog table tagged by <code>deployment_id</code>, letting Databricks SQL compare error-rate percentiles and auto-rollback underperforming versions.</li> <li><strong>SaaS Usage Metering</strong>: Insert per-tenant API-call counters via the HTTP plugin; a nightly Lakehouse query aggregates usage into invoices, eliminating custom metering micro-services.</li> <li><strong>Security Forensics Lake</strong>: Upload JSON batches of Suricata IDS events to a Unity Catalog volume using <code>PUT</code> commands, then run <code>COPY INTO</code> for near-real-time enrichment with Delta Live Tables, producing a searchable threat-intel lake that joins network logs with user session data.</li> </ol>
Feedback
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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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