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Overview
Datadog Observability Pipelines allows you to collect and process logs and metrics 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:
- Build and edit pipelines with guided templates.
- Deploy and manage Workers.
- Enable monitors to track pipeline health.
Get started
- Navigate to Observability Pipelines.
- Select a template based on your use case.
- Set up your pipeline:
- Choose a log source.
- Configure processors.
- Add one or more destinations.
- Install the Worker in your environment
- Enable monitors for real-time observability into your pipeline health.
See Set Up Pipelines for detailed instructions.
Common use cases and templates
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. |
| Template | Description |
|---|
| Metric Tag Governance | 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. |
See Explore templates for more information.
Further Reading
Additional helpful documentation, links, and articles: