The challenge
Bring order to chaotic telemetry
Logs arrive in dozens of formats with inconsistent fields and schemas. Without normalization, SOC teams face broken correlations, weak detection rules, and blind spots in threat visibility. Manual scripts for parsing and mapping add delays, errors, and constant maintenance.
The Solution
DataStream – intelligent security telemetry pipeline
Normalize and unify
DataStream converts raw logs into consistent, standardized schemas, making data ready for SIEMs, analytics platforms, and compliance workflows, without regex, custom parsers, or brittle scripts.
Map fields without manual work
Schema alignment happens automatically, removing the need for custom scripts or ongoing field mapping maintenance. This makes onboarding new data sources fast and consistent.
Detect schema drift automatically
DataStream monitors incoming logs against known schema definitions, flags missing, changed, or extra fields, and alerts you if format changes, so your normalization stays reliable even when vendors change log output.
Make your data AI-ready
With clean, standardized telemetry, AI agents deliver better outcomes: stronger correlations, fewer false positives, faster investigations, and automation you can trust. Normalization turns raw logs into a shared language, so your AI can work smarter from the start.
Key benefits
Why this approach works
Supported environments
One platform, no puzzle to assemble
You don’t need home-grown parsers, regex libraries, or a patchwork of normalization scripts. DataStream ships with ready-to-use Automation and Normalization Packs that instantly convert inconsistent logs into clean, standardized schemas.
Frequently asked questions
Which formats does DataStream normalize?
CSL, CEF, LEEF, JSON, OCSF, ECS, or native format logs are automatically aligned to a consistent schema.
What target destinations does DataStream support?
DataStream can deliver telemetry to multiple destinations, including SIEMs, analytics platforms, and storage systems (check all targets here).
Can we add our own transformations?
Yes. Use the low-code/no-code pipeline builder to extend parsing or add custom enrichment, without writing scripts.
How does this improve detection?
Normalized logs mean detection rules work consistently across all sources, cutting false positives and making correlations stronger.
Get DataStream on Azure Marketplace
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