NATS and Databricks Integration

Input and output integration overview

<p>The NATS Consumer Input Plugin enables real-time data consumption from NATS messaging subjects, integrating seamlessly into the Telegraf data pipeline for monitoring and metrics gathering.</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

NATS

<p>The NATS Consumer Plugin allows Telegraf to read metrics from specified NATS subjects and create metrics based on supported input data formats. Utilizing a Queue Group allows multiple instances of Telegraf to read from a NATS cluster in parallel, enhancing throughput and reliability. This plugin also supports various authentication methods, including username/password, NATS credentials files, and nkey seed files, ensuring secure communication with the NATS servers. It is particularly useful in environments where data persistence and message reliability are critical, thanks to features such as JetStream that facilitate the consumption of historical messages. Additionally, the ability to configure various operational parameters makes this plugin suitable for high-throughput scenarios while maintaining performance integrity.</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

NATS

Databricks

Input and output integration examples

NATS

<ol> <li> <p><strong>Real-Time Analytics Dashboard</strong>: Utilize the NATS plugin to gather metrics from various NATS subjects in real time and feed them into a centralized analytics dashboard. This setup allows for immediate visibility into live application performance, enabling teams to react swiftly to operational issues or performance degradation.</p> </li> <li> <p><strong>Distributed System Monitoring</strong>: Deploy multiple instances of Telegraf configured with the NATS plugin across a distributed architecture. This approach allows teams to aggregate metrics from various microservices efficiently, providing a holistic view of system health and performance while ensuring no messages are dropped during transmission.</p> </li> <li> <p><strong>Historical Message Recovery</strong>: Leverage the capabilities of NATS JetStream along with this plugin to recover and process historical messages after Telegraf has been restarted. This feature is particularly beneficial for applications that require high reliability, ensuring that no critical metrics are lost even in case of service disruptions.</p> </li> <li> <p><strong>Dynamic Load Balancing</strong>: Implement a dynamic load balancing scenario where Telegraf instances consume messages from a NATS cluster based on load. Adjust the queue group settings to control the number of active consumers, allowing for better resource utilization and performance scaling as demand fluctuations occur.</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>

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