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Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - 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Route logs to third-party systems with Datadog Log Forwarding
Addie Beach, Grace Gui, Pranay Kamat · 2022-10-13 · via Datadog | The Monitor blog

Large organizations often rely on multiple monitoring tools, security platforms, and auditing systems to meet the diverse needs of their observability, security, engineering, and compliance teams. Because these teams may use the same logs for many different use cases—including detecting potential threats or breaches, troubleshooting errors, and gauging the effectiveness of new features—it can be difficult to effectively standardize and route data. Additionally, organizations can struggle with tool sprawl in the absence of a strong central observability team, particularly as they acquire new teams or migrate to the cloud.

Datadog Log Pipelines offers a fully managed, centralized hub for your logs that is easy to set up. You can ingest logs from your entire stack, parse and enrich them with contextual information, add tags for usage attribution, generate metrics, and quickly identify log anomalies. You can also use Sensitive Data Scanner, standard attributes, and granular tagging to enforce organization-wide conventions and industry regulations. Distributing these processed logs to users across your enterprise can be a challenge, however, as some teams may prefer to use platforms or environments outside Datadog to accommodate certain workflows.

We are excited to announce that Log Pipelines supports Log Forwarding to custom destinations, now generally available. Log Forwarding enables you to centralize log processing, enrichment, and routing so that you can easily send your logs from Datadog to Splunk, Elasticsearch, or HTTP endpoints. By leveraging rich filtering options and routing logs to multiple destinations, you can provide standardized logs to your teams and easily manage a wide variety of logging use cases. With Log Forwarding, you can:

The Log Forwarding overview page in Datadog, showing Splunk, Elasticsearch, and HTTP endpoints.

Centralize log processing while accommodating flexible workflows

Datadog Log Pipelines allows you to ingest and transform your logs with features like grok parsing, remapping, and string extraction. Using Log Forwarding, you can take logs processed in Datadog pipelines and easily adapt them to the tools that work best for individual teams, with simple configuration and integration for your teams’ HTTP, Splunk, or Elasticsearch endpoints. This helps you centrally manage log processing, while still providing enough autonomy to your teams that they can efficiently analyze logs according to their specific requirements.

You can create custom destinations for external forwarding, then choose which logs you forward and how you forward them to fit your best practices. Log Forwarding provides you with role-based access control (RBAC) settings to manage who can create, edit or remove these destinations, helping you to fine-tune your security configuration and streamline audits. Additionally, Log Forwarding enables you to filter logs on an as-needed basis so that your teams receive only the data most relevant to them. This reduces the number of logs they need to store, cuts down on unnecessary noise, and helps prevent potential data leaks.

The configuration window for a custom third-party destination.

Let’s say your enterprise uses logs to monitor user authentication activity for troubleshooting system issues and detecting suspicious behavior. Your application and central observability teams have already adopted Datadog, but your security teams still follow established Security Information and Event Management (SIEM) workflows on a different observability tool. Using Log Forwarding, your central observability team can collect and process your logs in Log Pipelines, then easily forward the necessary logs to your security team’s external endpoint. This allows all of your enterprise users to work on their preferred platforms—your application teams can analyze their logs in Datadog using features like Application Performance Monitoring, and your security teams can still manage security threats using the application of their choice.

You can also use Log Forwarding to help your teams transition to new platforms. As you adopt Datadog Log Management, there may be certain teams that need to continue using existing solutions for contractual or business continuity reasons. You can easily ingest your logs in Datadog for centralized processing and parsing, then use Log Forwarding to route the logs to existing vendors. Each team can now migrate their workflows on their own schedule while still accessing standardized logs. Additionally, this dual shipping helps you ensure that downstream dependencies, such as data warehouses, continue to function as expected.

Enable compliance by duplicating logs across locations

Using Log Forwarding, you can send your logs to dedicated storage locations anywhere in the world. Geographically distributed organizations with many satellite offices—including government agencies, financial institutions, or insurance companies—often have to maintain copies of their logs for compliance or regulatory reasons. For example, some laws or standards stipulate that organizations must store their logs in a neutral location for a specified period of time or maintain local backups via their own file servers. Log Forwarding can help your teams access a consistent view of your logs no matter where they are located. This centralizes log pipelines, streamlines collaboration, and reduces mistakes that can result from users having outdated or inconsistent logs. Additionally, for longer-term storage, you can use Log Forwarding alongside Datadog’s existing archiving capabilities at no extra cost.

Shipping logs between locations also helps you facilitate projects with external partners or consultants. By providing an easy-to-use interface for building these integrations, Log Forwarding frees your teams from having to create custom tools for importing and exporting data. This allows them to focus on project tasks and deliver faster turnaround times.

Send processed logs to customized destinations with ease

With Datadog Log Forwarding, you get the best of both worlds—you can centrally process your logs according to industry regulations and your organization’s best practices while still giving your teams the autonomy they need to work effectively with their preferred tools. This flexibility enables you to easily support co-existence with other vendors, achieve compliance with industry regulations, ensure the accuracy of local backups, and streamline internal and external collaboration.

Log Forwarding is generally available to Datadog users. If you’re an existing customer, you can get started with Log Forwarding using our documentation. Or, if you’re not yet a Datadog customer, you can sign up for a 14-day free trial today.