Ingext
RecommendedIndependent streaming data fabric with native open lakehouse storage and unified search.
Recommended: full lifecycle, open, self-hostable.
Explore IngextThe Pipeline Layer Just Got Bought
When the upstream pipeline gets absorbed into a SIEM, XDR, lakehouse, or data platform, your data strategy starts following that vendor’s roadmap. CrowdStrike brought Onum into Falcon. SentinelOne brought Observo AI into Singularity. Databricks expanded Lakeflow for lakehouse ETL. Ingext remains the independent streaming data fabric for teams that need clean data to every tool, lower cost, and full control.
That independence matters because the upstream layer decides what every analytics system, AI workflow, SIEM, lakehouse, observability stack, and operational tool receives. Ingext collects once, cleans in motion, routes by value, keeps full-fidelity history in open storage, and stays deployable on infrastructure you control.
How to read this comparison
The buying question is not only “can it move data?”
Most tools can collect, filter, and route telemetry. The harder question is whether the platform helps you control cost, preserve choice, keep history searchable, and avoid rebuilding collection every time your analytics, AI, SIEM, lakehouse, observability, or operations stack changes.
Choose an ecosystem pipeline when...
You are all-in on Falcon, Singularity, Databricks, or another owner’s platform and want the fastest path inside that ecosystem.
Choose Ingext when...
You need a neutral data strategy layer that collects once, cleans in motion, routes by value, keeps open history, and remains yours as tools change.
Summary
Cribl Stream / Edge
Independent observability pipeline for collecting, reducing, enriching, and routing telemetry.
Strong routing; storage and search are separate surfaces.
See resultsCrowdStrike Onum
Real-time telemetry pipeline now operating inside the CrowdStrike Falcon ecosystem.
Strong for Falcon-first teams; no longer neutral.
See resultsSentinelOne Observo
AI-powered data pipeline capability aligned to SentinelOne Singularity and AI SIEM.
Useful inside Singularity; constrained in mixed stacks.
See resultsDatabricks Lakeflow
Lakehouse pipeline and ETL platform for engineering data into Databricks.
Different category: excellent lakehouse ETL, not a neutral telemetry fabric.
Explore IngextWhy Streaming Data Fabrics Exist
The goal of a streaming data fabric is to free every downstream tool from collection, normalization, and storage duties that belong in a dedicated upstream layer.
When collection and routing are tied directly to one downstream platform, every change becomes painful. Replacing or upgrading analytics, AI, SIEM, lakehouse, or observability tools can mean rebuilding collection from scratch. Without a unified data management layer, each destination grows its own filters, uptime checks, schemas, and routing logic.
“A streaming data fabric turns data chaos into reusable structure.”
A streaming data fabric provides a single, independent layer where data can be collected, transformed, filtered, stored, searched, 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 comes down to cost, control, speed, and trust. Every inefficiency in collection, storage, or routing is waste. A streaming data fabric turns operational data into reusable structure that saves money while enabling smarter decisions across the organization.
How to Evaluate a Streaming Data Fabric
Before comparing vendors, identify four control questions. Platforms that miss one are usually a router, lakehouse, or suite feature rather than a true independent fabric.
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 data 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 placement with use: urgent signals to real-time tools, dense telemetry to lakehouse storage, noise dropped before it consumes budget.
4. Independence
Does it stay neutral across your tools and clouds, or does the roadmap answer to one SIEM, XDR, AI, lakehouse, or data platform owner?
Comparing the Leading Streaming Data Fabrics: A Practical Framework
This page compares the leading streaming data fabric, telemetry pipeline, ecosystem pipeline, and lakehouse ETL options shaping operational data strategy today.
Ingext
Independent streaming data fabric with native open lakehouse storage and unified search.
Recommended: full lifecycle, open, self-hostable.
Cribl Stream / Edge
Independent observability pipeline for collecting, reducing, enriching, and routing telemetry.
Strong routing; storage and search are separate surfaces.
Detailed AnalysisCrowdStrike Onum
Real-time telemetry pipeline now operating inside the CrowdStrike Falcon ecosystem.
Strong for Falcon-first teams; no longer neutral.
Detailed AnalysisSentinelOne Observo
AI-powered data pipeline capability aligned to SentinelOne Singularity and AI SIEM.
Useful inside Singularity; constrained in mixed stacks.
Detailed AnalysisDatabricks Lakeflow
Lakehouse pipeline and ETL platform for engineering data into Databricks.
Different category: excellent lakehouse ETL, not a neutral telemetry fabric.
Detailed Evaluation Framework
The framework below expands on the four control questions with detailed technical criteria used to evaluate each platform.
Stage 1 — Must-Have Gates
Missing any gate means a product is likely a router, lakehouse, or suite feature rather than an independent 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:
- Real-time tools: High-value operational signals, enriched and normalized inline.
- Lakehouse: Dense telemetry for low-cost storage, large-scale analytics, and AI.
- 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 flow layer should feed multiple domains — analytics, AI, SIEM, metrics stores, observability platforms, and 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 flow platforms 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 all four gates: transform, continuity, routing, and independence
Ingext is the independent, full-lifecycle streaming data fabric: collect once, clean in motion, route by value, keep full-fidelity history in open Parquet, and search live and stored data without rehydration.
Cribl Stream / Edge
Strong independent telemetry pipeline; storage/search are separate surfaces
Cribl remains the category creator for observability pipelines. It is strong for collecting, reducing, enriching, and routing telemetry from many sources to many tools, while full lifecycle storage and search usually mean more products and more pipeline engineering.
CrowdStrike Onum
Strong real-time security pipeline inside Falcon
Falcon Onum is now CrowdStrike’s real-time data pipeline and security data control plane. That is compelling for Falcon-first teams, but it changes the independence question for organizations that need a neutral layer across analytics, AI, SIEM, lakehouse, and observability destinations.
SentinelOne Observo
Useful AI data pipeline inside Singularity
SentinelOne’s AI Data Pipelines, strengthened by Observo AI, reduce noisy telemetry, normalize records, and feed AI SIEM and Singularity Data Lake. The strongest fit is inside SentinelOne; mixed stacks still need a neutral upstream fabric.
Databricks Lakeflow
Different category: lakehouse ETL, not a neutral telemetry fabric
Lakeflow is excellent for engineering batch and streaming data into the Databricks lakehouse for analytics and AI. Ingext can feed Databricks cleaner operational data without making collection, routing, retention, and search live inside one data platform.
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 | Databricks Lakeflow |
|---|---|---|---|---|---|
| Transformation | Full inline parsing & enrichment | Strong transform and reduction | Inline transformation of Falcon telemetry | Real-time normalization and enrichment | Declarative ETL into Databricks tables |
| Streaming Continuity | Continuous flow with buffering, retries, and replay | Real-time streaming with replay | Continuous streaming to multiple storage layers | Built for streaming ingestion | Batch and streaming ETL pipelines |
| Routing | Multi-sink routing: analytics, AI, SIEM, lake, archive | Flexible routing rules | Strongest toward Falcon-native paths | Strongest toward Singularity paths | Routes into Databricks lakehouse workflows |
| Filtering | Inline drop with structured logic support | Filter and mask support | Inline reduction and normalization | Inline filtering and tagging | ETL filtering after platform ingestion |
| Output Versatility | Analytics, AI, metrics, SIEMs, and data lakes | Multiple destinations supported | Falcon-native outputs | Primarily SentinelOne outputs | Databricks ecosystem outputs |
| Processing Logic | Full ES6 Declarative logic | Limited logic, YAML configuration | Limited open logic layer | Limited user-defined logic | SQL/Python data engineering logic |
| Agnostic Deployment | Vendor-agnostic across hybrid/cloud | Independent and broadly vendor-agnostic | Proprietary within Falcon ecosystem | Closed ecosystem (Singularity only) | Databricks platform economics and governance |
| Native Storage + Search | Open Parquet lakehouse and unified search | Lake and Search are separate products | Hands off to Falcon storage/search | Searchable inside SentinelOne | Excellent lakehouse search inside Databricks |
Key Takeaways
Independence is now a buying criterion. Onum and Observo moved into major security platforms; neutrality can disappear after acquisition.
Ingext and Cribl are the two clearest independent choices for heterogeneous enterprise environments.
Ingext differentiates on full lifecycle. It combines collection, inline transformation, routing, open lakehouse retention, and unified search in one self-hostable system.
The future of streaming data fabrics is open, streaming, and upstream of every analytics, AI, SIEM, lakehouse, observability, and operational tool.
Conclusion
A true streaming data fabric must transform, enrich, route, store, search, and reuse data while it flows. A router alone is not enough. A lakehouse alone is not enough. A suite-owned pipeline is not neutral enough.
Among current options, Ingext provides the clearest independent implementation of that vision: real-time transformation, cost-aware routing, native open lakehouse retention, unified search, and deployment freedom.
Take the Next Step
Every organization faces the same scaling limits as operational data volume grows. A streaming data fabric restores control, reduces downstream cost, and lets you modernize without rebuilding collection every time an analytics, AI, SIEM, lakehouse, observability, or operations tool changes.