Kinesis and Databricks Integration

Input and output integration overview

<p>The Kinesis plugin enables you to read from Kinesis data streams, supporting various data formats and configurations.</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

Kinesis

<p>The Kinesis Telegraf plugin is designed to read from Amazon Kinesis data streams, enabling users to gather metrics in real-time. As a service input plugin, it operates by listening for incoming data rather than polling at regular intervals. The configuration specifies various options including the AWS region, stream name, authentication credentials, and data formats. It supports tracking of undelivered messages to prevent data loss, and users can utilize DynamoDB for maintaining checkpoints of the last processed records. This plugin is particularly useful for applications requiring reliable and scalable stream processing alongside other monitoring needs.</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

Kinesis

Databricks

Input and output integration examples

Kinesis

<ol> <li> <p><strong>Real-Time Data Processing with Kinesis</strong>: This use case involves integrating the Kinesis plugin with a monitoring dashboard to analyze incoming data metrics in real-time. For instance, an application could consume logs from multiple services and present them visually, allowing operations teams to quickly identify trends and react to anomalies as they occur.</p> </li> <li> <p><strong>Serverless Log Aggregation</strong>: Utilize this plugin in a serverless architecture where Kinesis streams aggregate logs from various microservices. The plugin can create metrics that help detect issues in the system, automating alerting processes through third-party integrations, enabling teams to minimize downtime and improve reliability.</p> </li> <li> <p><strong>Dynamic Scaling Based on Stream Metrics</strong>: Implement a solution where stream metrics consumed by the Kinesis plugin could be used to adjust resources dynamically. For example, if the number of records processed spikes, corresponding scale-up actions could be triggered to handle the increased load, ensuring optimal resource allocation and performance.</p> </li> <li> <p><strong>Data Pipeline to S3 with Checkpointing</strong>: Create a robust data pipeline where Kinesis stream data is processed through the Telegraf Kinesis plugin, with checkpoints stored in DynamoDB. This approach can ensure data consistency and reliability, as it manages the state of processed data, enabling seamless integration with downstream data lakes or storage solutions.</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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