Data Fabrics are no longer Optional

The way we move data is broken. Every SIEM on the market is choking on its own telemetry, and vendors are finally admitting it. That’s why SentinelOne bought Observo AI. That’s why CrowdStrike grabbed Onum. And that’s why Cribl built its empire as Splunk’s unofficial lifeline. They’re all racing to patch the problem Ingext solved years ago: the absence of a true, independent data fabric.

Without one, you can’t scale, you can’t control cost, and you sure as hell can’t make sense of your data. Ingext isn’t a bolt-on or a plugin — it’s the backbone that makes modern telemetry possible. Where others are locking customers into closed ecosystems, Ingext remains open, agnostic, and purpose-built for the reality every enterprise faces: too much data, not enough clarity. The industry is just waking up to a truth we’ve been living for years — without a data fabric, your SIEM is just a storage problem pretending to be a solution.

The Power of the Data Fabric

A data fabric delivers what every downstream system depends on: cleaner data, faster performance, and lower cost. It’s the layer that makes everything else: SIEMs, data lakes, APMs, observability tools, actually work as intended. By handling collection, parsing, enrichment, and routing before the data ever reaches those products, the fabric turns chaos into clarity.

The value is simple and measurable. The fabric reduces what goes in, which means everything that follows runs faster and costs less.

This consolidation isn’t just cleaner architecture; it’s better economics.

• Less data means reduced storage and licensing costs.
• Centralized enrichment means higher-quality context across every tool.
• Shared pipelines mean fewer moving parts to manage or break.

The result is enhanced capability at lower cost, a unified data backbone that lets security, operations, and performance platforms all draw from the same high-quality stream. It’s not about fixing one system. It’s about simplifying the entire enterprise data flow so every system performs at its best.

One Size Does Not Fit All

A data fabric does not force a single normalized record on every tool. It applies destination-aware transforms. The same event can be shaped three ways at once: a high-signal version for the SIEM, an application-centric version for the APM, and a rich, lossless version for the data lake.

One collection pipeline. Many outputs.

Per route, the fabric decides how to parse, enrich, redact, and structure the record. SIEM needs user, asset, and policy context with tight schemas. APM needs latency, trace, and dependency fields. The lake wants full fidelity with efficient columnar storage. Each gets exactly what it needs.

This is the point of a fabric: policy-driven shaping and routing. Keep what matters, drop what does not, mask what must be private, and send the right form to the right place. Ingext was built for this multi-target reality, so you do not bend every downstream tool to a fake “universal” schema. You let the fabric tailor the payload, and the whole stack runs cleaner and cheaper.

The Missed Point: Vendor Piping Is Not a Data Fabric

Flexibility is what the imitators missed. Onum and Observo didn’t invent data fabrics: they misunderstood them. Both products are imitations of Cribl, but stripped down to a fraction of its function. They implement the easiest parts, parsing and dropping, and stop there. That’s not innovation; that’s reduction. Parsing and filtering may lighten the load on a SIEM, but they don’t make a data fabric.

A real data fabric does more than clean data on the way in. It understands the data and decides where it belongs. It recognizes whether a message carries operational metrics, security telemetry, or compliance records, and routes it accordingly. That intelligence is the difference between a parsing tool and a routing engine.

By calling these limited parsers “data fabrics,” the industry is mistaking a broom for a blueprint. They’re solving a symptom, ingestion volume, while ignoring the larger goal: creating an adaptive data layer that serves the whole ecosystem, not just one product.

It’s also all not about efficiency. Fabrics make decisions. It recognizes every record, understands its purpose, and then routes it intelligently, not just to one destination, but to many. Security telemetry might flow to the SIEM, while performance metrics go to an APM, and full-fidelity logs are archived in one or more data lakes for research, compliance, or AI modeling. Some events get dropped altogether. Each path is tuned to what the receiving system actually needs.

When the “fabric” only optimizes for a single platform, the entire advantage collapses.

That’s the trap these new acquisitions have fallen into, data pipelines built to feed one ecosystem instead of powering an organization’s ecosystem. It’s great for the vendor; it’s terrible for the customer. The more you tune your pipeline to one tool’s schema, the harder it becomes to use that same data elsewhere. Flexibility dies the moment the plumbing serves one master.

That’s why Ingext and Cribl both evolved beyond simple transform-and-drop engines. They added search, storage, and routing intelligence, because a true fabric doesn’t just reduce data volume; it directs data value. The end goal isn’t to thin the stream for a SIEM. It’s to orchestrate the entire flow of enterprise telemetry so that each downstream system, security, performance, analytics, gets the version of truth it actually needs.

Where the Fabric Has to Go

If Onum and Observo are missteps, it’s because they mistake transformation for processing. The future of the data fabric isn’t about changing shapes — it’s about executing logic. Ingext proves this. It’s not just parsing, enriching, or dropping. It’s processing: running decisions, evaluating context, and applying logic as data moves. That’s what separates a parser from a platform.

These newer tools do a poor job of enrichment because they don’t actually process anything. They take one shape of data, force it into another, and call it a day. That’s not enrichment — that’s rearrangement. True enrichment happens when the fabric can evaluate, correlate, and act: when it can decide that a certain field needs a lookup, that a relationship should trigger routing to another system, or that an event matches a known context.

Cribl approaches this by hiding processing behind pre-coded middleware services, which works, but it’s still closed. Ingext gives that power directly to the user. You can build logic, not just templates. You can code behavior, not just field maps. That’s the real distinction: Ingext isn’t a shape-shifter; it’s a processor. And in an industry drowning in data, the ability to apply logic in motion is what turns flow into intelligence.

These Acquisitions Are a Wake-Up Call

Let’s be clear. These acquisitions are not about innovation. They are about survival. SentinelOne and CrowdStrike did not suddenly realize the value of a data fabric. They bought Observo and Onum because the lack of proper data piping was costing them money. Their customers were overloaded with telemetry, their performance was declining, and storage costs were rising fast. The answer was not another analytics feature. It was fixing the way data moves. Rather than rebuild their products, they bought a shortcut.

That tells you everything. This is not a passing trend. The market has reached a point where data movement is a critical element to success. A platform cannot grow or hold customers if its data layer is broken. These acquisitions show that data fabrics are now essential parts of the architecture. But buying one small parser and calling it a fabric does not solve the problem.

The simple truth is that these are stopgaps, not revolutions. They hold revenue steady but do not change the foundation. That is why the independent vendors such as Ingext and Cribl are on a different course. They are building solutions for users, not for earnings reports. Their focus is on interoperability and long-term design, not brand containment. They are fixing the flow, not the optics.

These purchases are a wake-up call, but they are also a warning. A data fabric that exists only to protect a single product will not survive the next wave. The real progress comes from open, agnostic systems that serve every downstream tool. Ingext was built for that purpose: not to patch the problem, but to end it.