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Overview
Datadog Observability Pipelines allows you to collect and process logs and metrics (PREVIEW indicates an early access version of a major product or feature that you can opt into before its official release.Glossary) within your own infrastructure, and then route the data to different destinations. It gives you control over your observability data before it leaves your environment.
With out-of-the-box templates, you can build pipelines that redact sensitive data, enrich data, filter out noisy events, and route data to destinations like Datadog, SIEM tools, or cloud storage.
Key components
Observability Pipelines Worker
The Observability Pipelines Worker runs within your infrastructure to aggregate, process, and route data.
Datadog recommends you update Observability Pipelines Worker (OPW) with every minor and patch release, or, at a minimum, monthly.
Upgrading to a major OPW version and keeping it updated is the only supported way to get the latest OPW functionality, fixes, and security updates. See Upgrade the Worker to update to the latest Worker version.
Observability Pipelines UI
The Observability Pipelines UI provides a centralized control plane where you can:
Observability Pipelines includes prebuilt templates for common data routing and transformation workflows. You can fully customize or combine them to meet your needs.
Templates
Template
Description
Archive Logs
Store raw logs in Amazon S3, Google Cloud Storage, or Azure Storage for long-term retention and rehydration.
Dual Ship Logs
Send the same log stream to multiple destinations (for example, Datadog and a SIEM).
Generate Log-based Metrics
Convert high-volume logs into count or distribution metrics to reduce storage needs.
Log Enrichment
Add metadata from reference tables or static mappings for more effective querying.
Log Volume Control
Reduce indexed log volume by filtering low-value logs before they’re stored.
Sensitive Data Redaction
Detect and remove personally identifiable information (PII) and secrets using built-in or custom rules.
Split Logs
Route logs by type (for example, security vs. application) to different tools.
Metrics Volume and Cardinality Control is in Preview. Fill out the form to request access.
Template
Description
Metrics Volume and Cardinality Control
Manage the quality and volume of your metrics by keeping only the metrics you need, standardizing metrics tagging, and removing unwanted tags to prevent high cardinality.
