Vector

Configure Vector to collect logs from a source (e.g., Kafka).

Use the VRL transform to redact sensitive data using regular expressions or built-in filters.

Route the transformed logs to a sink (e.g., Elasticsearch).

Verify that the sensitive data has been successfully redacted.

Deploy Vector agents on each source.

Configure each agent to forward logs to a central Vector aggregator.

Configure the aggregator to receive and process logs from all agents.

Route the aggregated logs to a central sink (e.g., Splunk).

Configure Vector to collect logs from a source.

Use the VRL transform to extract metrics from the logs (e.g., error counts, request latency).

Route the extracted metrics to a metrics sink (e.g., Prometheus).

Deploy Vector as a DaemonSet in the Kubernetes cluster.

Configure Vector to collect logs and metrics from all pods.

Route the data to monitoring systems (e.g., Datadog, Prometheus).

Set up alerts based on the collected metrics.

Configure Vector to receive data from Splunk HEC endpoint.

Configure Vector to send data to Datadog.

Define transformations, if needed.

Ensure API keys are correctly configured.