Ingext
RecommendedUnified streaming data fabric for security and observability.
Fully meets all gates; designed for open, hybrid control.
Explore IngextCompare the Leading Data Fabrics for SIEM
Modern SIEMs fail not because they analyze poorly — but because they collect poorly. This page helps you evaluate which data fabric restores control, cost, and continuity to your security operations.
Learn how each leading vendor approaches transformation, routing, and flow management — and why these capabilities determine whether a platform is a true streaming data fabric or just another integration tool.
Summary
Cribl Stream / Edge
Widely used observability routing platform with mature tooling.
Partially meets; observability-centric, YAML-dependent.
See resultsCrowdStrike Onum
CrowdStrike-native streaming fabric that brings Falcon telemetry control in-house.
Strong inside the Falcon ecosystem today; limited flexibility outside it.
See resultsSentinelOne Observo
Streaming layer aligned with SentinelOne’s Singularity stack, adding data routing to the platform.
Best fit for SentinelOne customers as integration deepens; weaker in heterogeneous stacks.
See resultsWhy Data Fabrics Exist
The goal of a data fabric isn't to replace your SIEM — it's to free it. SIEMs fail to scale because they've inherited collection, normalization, and storage duties that belong in a dedicated layer.
When collection and routing are tied directly to the SIEM, every change becomes painful. Replacing or upgrading the SIEM means rebuilding the entire collection infrastructure from scratch. Without a unified data management layer, you end up maintaining two separate infrastructures — one for the SIEM and another for the data lake — each with its own filters, uptime checks, and routing logic.
“A streaming data fabric doesn’t just modernize the SIEM architecture — it turns chaos into structure.”
A data fabric provides a single, independent layer where data can be collected, filtered, and routed before reaching any tool. That flexibility allows modernization to happen incrementally, not as a massive, high-risk overhaul.
And at its core, this all comes down to cost. Every inefficiency in collection, storage, or routing is a form of financial waste. A streaming data fabric turns the chaos of data management into a structured, efficient system that saves money while enabling smarter decisions across the organization.
How to Evaluate a Data Fabric
Before comparing vendors, identify three control questions. Platforms that can't meet these three gates aren't true data fabrics.
1. Transformation
Can the platform enrich and normalize data inline, before storage? Without inline transformation, you're just moving raw data — not solving the mismatched schemas that generate investigation drag.
2. Continuity
Can it guarantee uninterrupted flow with retry, buffering, and source monitoring? A true streaming fabric absorbs congestion and prevents backflow so sources and destinations stay in sync.
3. Routing Flexibility
Can it direct data to multiple destinations — or drop it — based on value? Align storage with use: notables in the SIEM, telemetry in data lakes, noise dropped before it consumes budget.
Comparing the Leading Data Fabrics: A Practical Framework
This page compares the four leading data fabric platforms built to handle streaming telemetry at enterprise scale.
Ingext
Unified streaming data fabric for security and observability.
Fully meets all gates; designed for open, hybrid control.
Cribl Stream / Edge
Widely used observability routing platform with mature tooling.
Partially meets; observability-centric, YAML-dependent.
Detailed AnalysisCrowdStrike Onum
CrowdStrike-native streaming fabric that brings Falcon telemetry control in-house.
Strong inside the Falcon ecosystem today; limited flexibility outside it.
Detailed AnalysisSentinelOne Observo
Streaming layer aligned with SentinelOne’s Singularity stack, adding data routing to the platform.
Best fit for SentinelOne customers as integration deepens; weaker in heterogeneous stacks.
Detailed AnalysisDetailed Evaluation Framework
The framework below expands on the three control questions with detailed technical criteria used to evaluate each platform.
Stage 1 — Must-Have Gates
Failing either gate disqualifies a product from being considered a true streaming data fabric.
Transformation (Parsing)
Inline parsing, normalization, timestamp correction, and enrichment before storage. Ensures data becomes usable as it flows, not after the fact.
Streaming Continuity
Continuous, unpaused flow with buffering, retries, and rolling upgrades. Guarantees reliability and low latency even during bursts or outages.
Stage 2 — Technical Evaluation Criteria
Routing
The greatest gain comes from putting data where it delivers value. A streaming fabric should route to:
- SIEM: High-value events, enriched and normalized inline.
- Data Lake: Dense telemetry for low-cost storage and large-scale analytics.
- Drop: Roughly 35–40% noise removed before ingestion, with another 95% of telemetry shifting to cheaper tiers.
The point isn’t less collection — it’s smarter placement.
Filtering / Dropping
Inline filters remove redundant telemetry before it hits storage. Around 40% of collected data can be safely dropped, reducing downstream cost and noise.
Output Versatility
One fabric should feed multiple domains — SIEM, metrics stores, data lakes — without maintaining separate collection stacks.
Processing Logic
Declarative or rule-based computation in motion lets you enrich, transform, and score records before they land.
Agnostic Deployment
True fabrics operate across cloud, hybrid, or on-prem environments with open interfaces, avoiding vendor lock-in and supporting future modernization.
Summary of the Analysis
Ingext
Passes both Gates and Stage 2 criteria
Ingext represents the next generation of data fabrics: a true streaming system purpose-built for security telemetry. It performs inline parsing and enrichment, applies conditional routing and filtering in real time, and supports multi-destination delivery across SIEM, metrics, and data lake tiers. Ingext's architecture is vendor-agnostic, enabling flexible deployment in hybrid and multi-cloud environments.
Cribl Stream / Edge
Passes both Gates, partial in processing logic and agnosticism
Cribl remains the most common choice in observability pipelines. It provides strong routing and filtering but depends heavily on YAML logic and centralized cloud components. While capable of transformation and real-time replay, its operational focus remains on observability and log management rather than SIEM-scale telemetry control.
CrowdStrike Onum
Passes both Gates, optimized for Falcon environments
Onum was acquired to fill a gap left by Cribl, providing CrowdStrike customers with native streaming and routing for Falcon data. It supports inline transformation and continuous flow and will likely integrate more tightly with Falcon over time, but remains limited for mixed-environment SIEM use.
SentinelOne Observo
Passes both Gates, proprietary scope prevents cross-SIEM flexibility
SentinelOne Observo (previously Observio) delivers a streaming layer inside the Singularity ecosystem. It offers real-time enrichment and routing benefits to SentinelOne customers and is expected to integrate more deeply across their product line, but has minimal support for heterogeneous stacks.
Detailed Comparison
The table below shows how each platform performs against the evaluation framework. Use this to understand where each vendor excels and where limitations may impact your specific use case.
| Category | Ingext | Cribl Stream / Edge | CrowdStrike Onum | SentinelOne Observo |
|---|---|---|---|---|
| Transformation | Full inline parsing & enrichment | Strong transform and reduction | Inline transformation of Falcon telemetry | Real-time normalization and enrichment |
| Streaming Continuity | Continuous flow with buffering, retries, and replay | Real-time streaming with replay | Continuous streaming to multiple storage layers | Built for streaming ingestion |
| Routing | Multi-sink routing (SIEM, Lake, Archive) | Flexible routing rules | Multi-output routing | Multi-output routing |
| Filtering | Inline drop with structured logic support | Filter and mask support | Inline reduction and normalization | Inline filtering and tagging |
| Output Versatility | Metrics, SIEMs, and data lakes | Multiple destinations supported | Falcon-native outputs | Primarily SentinelOne outputs |
| Processing Logic | Full ES6 Declarative logic | Limited logic, YAML configuration | Limited open logic layer | Limited user-defined logic |
| Agnostic Deployment | Vendor-agnostic across hybrid/cloud | Primarily observability-focused | Proprietary within Falcon ecosystem | Closed ecosystem (Singularity only) |
Key Takeaways
Only a few platforms truly perform inline parsing and maintain uninterrupted streaming under SIEM-scale load.
Ingext and Cribl are the only two vendor-agnostic fabrics suitable for enterprise multi-source environments.
Onum and SentinelOne Observo deliver increasing value inside their native ecosystems but remain constrained in heterogeneous deployments.
The future of SIEM data fabrics lies in open, streaming architectures that transform and route data at ingestion, not after storage.
Conclusion
A true SIEM Data Fabric must transform, enrich, and route data while it flows — not rely on post-ingest processing or vendor lock-in.
Among current options, Ingext provides the most complete and open implementation of that vision, merging real-time transformation with cost-aware routing and full deployment freedom.
Take the Next Step
Every organization faces the same scaling limits in SIEM operations. A streaming data fabric restores control, reduces cost by up to 80%, and enables incremental modernization without high-risk overhauls.