Hashicorp Vault and Databricks Integration

Input and output integration overview

The Hashicorp Vault plugin for Telegraf allows for the collection of metrics from Hashicorp Vault services, facilitating monitoring and operational insights.

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.

Integration details

Hashicorp Vault

The Hashicorp Vault plugin is designed to collect metrics from Vault agents running within a cluster. It enables Telegraf, an agent for collecting and reporting metrics, to interface with the Vault services, typically listening on a local address such as http://127.0.0.1:8200. This plugin requires a valid token for authorization, ensuring secure access to the Vault API. Users must configure either a token directly or provide a path to a token file, enhancing flexibility in authentication methods. Proper configuration of the timeout and optional TLS settings further relates to the security and responsiveness of the metrics collection process. As Vault is a critical tool in managing secrets and protecting sensitive data, monitoring its performance and health through this plugin is essential for maintaining operational security and efficiency.

Databricks

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 statement field. Each Telegraf flush dynamically renders a SQL INSERT (or, for file-based workflows, a PUT ... INTO /Volumes/... 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 warehouse_id 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 COPY INTO are preferred, and the approach aligns with Databricks’ recommended ingestion practices for partners and ISVs.

Configuration

Hashicorp Vault

[[inputs.vault]]
  ## URL for the Vault agent
  # url = "http://127.0.0.1:8200"

  ## Use Vault token for authorization.
  ## Vault token configuration is mandatory.
  ## If both are empty or both are set, an error is thrown.
  # token_file = "/path/to/auth/token"
  ## OR
  token = "s.CDDrgg5zPv5ssI0Z2P4qxJj2"

  ## Set response_timeout (default 5 seconds)
  # response_timeout = "5s"

  ## Optional TLS Config
  # tls_ca = /path/to/cafile
  # tls_cert = /path/to/certfile
  # tls_key = /path/to/keyfile

Databricks

[[outputs.http]]
  ## Databricks SQL Statement Execution API endpoint
  url = "https://{{ env "DATABRICKS_HOST" }}/api/2.0/sql/statements"

  ## Use POST to submit each Telegraf batch as a SQL request
  method = "POST"

  ## Personal-access token (PAT) for workspace or service principal
  headers = { Authorization = "Bearer {{ env "DATABRICKS_TOKEN" }}" }

  ## Send JSON that wraps the metrics batch in a SQL INSERT (or PUT into a Volume)
  content_type = "application/json"

  ## Serialize metrics as JSON so they can be embedded in the SQL statement
  data_format = "json"
  json_timestamp_units = "1ms"

  ## Build the request body.  Telegraf replaces the template variables at runtime.
  ## Example inserts a row per metric into a Unity-Catalog table.
  body_template = """
  {
    \"statement\": \"INSERT INTO ${TARGET_TABLE} VALUES {{range .Metrics}}(from_unixtime({{.timestamp}}/1000), {{.fields.usage}}, '{{.tags.host}}'){{end}}\",
    \"warehouse_id\": \"${WAREHOUSE_ID}\"
  }
  """

  ## Optional: add batching limits or TLS settings
  # batch_size = 500
  # timeout     = "10s"

Input and output integration examples

Hashicorp Vault

1. Centralized Secret Management Monitoring: Utilize the Vault plugin to monitor multiple Vault instances across a distributed system, allowing for a unified view of secret access patterns and system health. This setup can help DevOps teams quickly identify any anomalies in secret access, providing essential insights into security postures across different environments.

2. Audit Logging Integration: Configure this plugin to feed monitoring metrics into an audit logging system, enabling organizations to have a comprehensive view of their Vault interactions. By correlating audit logs with metrics, teams can investigate issues, optimize performance, and ensure compliance with security policies more effectively.

3. Performance Benchmarking During Deployments: During application deployments that interact with Vault, use the plugin to monitor the effects of those deployments on Vault performance. This allows engineering teams to understand how changes impact secret management workflows and to proactively address performance bottlenecks, ensuring smooth deployment processes.

4. Alerting for Threshold Exceedance: Integrate this plugin with alerting mechanisms to notify administrators when metrics exceed predefined thresholds. This proactive monitoring can help teams respond swiftly to potential issues, maintaining system reliability and uptime by allowing them to take action before any serious incidents arise.

Databricks

1. Edge-to-Lakehouse Telemetry Pipe: 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.
2. Blue-Green CI/CD Rollout Metrics: Attach a Telegraf sidecar to each Kubernetes canary pod; it inserts container stats into a Unity Catalog table tagged by deployment_id, letting Databricks SQL compare error-rate percentiles and auto-rollback underperforming versions.
3. SaaS Usage Metering: Insert per-tenant API-call counters via the HTTP plugin; a nightly Lakehouse query aggregates usage into invoices, eliminating custom metering micro-services.
4. Security Forensics Lake: Upload JSON batches of Suricata IDS events to a Unity Catalog volume using PUT commands, then run COPY INTO for near-real-time enrichment with Delta Live Tables, producing a searchable threat-intel lake that joins network logs with user session data.

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