Axoflow + Dynatrace: Reduced, AI‑Ready Telemetry Data

AxoRouter supports sending data to the Dynatrace cloud collector and to local ActiveGate collectors as well.

Axoflow optimizes ingestion costs by reducing data volume before it ever reaches your Dynatrace environment — while preserving the fidelity of the security telemetry your analysts rely on. Here’s how it works::

• Parse and normalize in the pipeline > Send only meaningful, well-structured data into Dynatrace
  Axoflow processes incoming data in the pipeline, classifying and parsing events into structured formats early in the flow. By identifying what fields and values actually matter for detection, correlation, and compliance, Axoflow filters out unnecessary payload elements and redundant metadata.
• Smart filtering and field reduction.
  Through flexible routing and filtering policies, Axoflow can remove repetitive fields, drop unneeded event types, and normalize vendor-specific formats according to Dynatrace Grail normalization format. For example, a firewall log stream can be reduced by 30–50% without losing any detection-relevant information.
  
• Enrich once — not everywhere.
  Instead of enriching data repeatedly inside Dynatrace, Axoflow performs enrichment upstream — for example, tagging logs with geolocation or asset context as they pass through the pipeline. This ensures enrichment is done once and stored efficiently, keeping indexed data lean and consistent.
• Use dynamic routing to control what goes where.
  Axoflow allows you to route high-value or high-context events directly into Dynatrace while diverting verbose or low-value telemetry to a cheaper storage tier (e.g., object storage, data lake, or SIEM cold storage). That way, Dynatrace only receives the data needed for search, detection, and dashboards — without losing the ability to access full-fidelity data later if needed.
• Monitor and tune with pipeline metrics.
  Axoflow provides detailed metrics on data volume, event types, and transformation stages. You can visualize what’s contributing most to ingestion size and adjust filters or parsers accordingly — ensuring that your Dynatrace license is used for the highest-value data.

Most tools simply push raw or lightly transformed data into the ingestion endpoint of the Dynatrace API. Axoflow integrates at the pipeline level, meaning that:

• Logs are classified, parsed, and normalized before they reach Dynatrace.
• Data is aligned automatically with Dynatrace Grail’s semantic dictionary, eliminating manual field mapping.
• Noise reduction, deduplication, and enrichment happen upstream — not inside Dynatrace.

The result is structured, AI-ready data that works immediately with Dynatrace dashboards, queries, SLOs, and ML models — without brittle custom parsing rules or constant maintenance.

Axoflow doesn’t just connect to Dynatrace. It prepares your data to fully leverage it.

Yes. Axoflow was built with multi-destination delivery by design.

You can send the same event stream to Dynatrace and other platforms (such as data lakes, cold storage, or additional SIEMs), with transformations, normalization, and formatting optimized for each destination individually.

For example:

• Send normalized, reduced, AI-ready data to Dynatrace for real-time detection and dashboards.
• Store full-fidelity raw logs in object storage for compliance or long-term retention.
• Mirror traffic during migrations to validate data quality before switching tools.

This decoupled pipeline architecture prevents vendor lock-in, reduces risk during migrations, and ensures each system receives exactly the data format it performs best with.

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AI and advanced analytics are only as good as the data they’re trained and queried on. Axoflow improves Dynatrace performance by:

• Reducing noise before ingestion → fewer false positives and cleaner training data.
• Standardizing fields across vendors → consistent attributes for correlation and modeling.
• Enriching records with identity, cloud, and Kubernetes metadata → stronger context for anomaly detection.
• Eliminating redundant fields and duplicates → faster queries and lower compute usage.

Because data arrives pre-classified, normalized, and enriched, Dynatrace’s analytics engine can focus on signal instead of cleaning up telemetry. This leads to faster investigations, more accurate alerts, and more reliable AI-driven insights.

Axoflow fixes data quality in the pipeline — so your downstream AI stays fast, accurate, and cost-efficient.