Why We Need a Data Fabric

Before talking about the benefits, we must understand why a data fabric exists at all.

Most organizations rely on more than one system to get work done. Email platforms, cloud services, security tools, and financial systems all generate their own data, but they rarely talk to each other directly. Yet we often need these systems to share information. Sometimes it’s for visibility, such as monitoring network activity or tracking security alerts. Other times it’s for coordination, such as keeping cloud records aligned with what’s in an internal database.

The problem is that each system stores and outputs its data differently. What one system calls a “log,” another calls a “record.” Even when both describe the same event, their formats, fields, and timestamps don’t match. So when we try to bring these systems together, we find that the challenge isn’t just collecting the data. It’s making that data understandable and usable across different tools.

Tools like Zapier, n8n, or make.com illustrate this problem in miniature. They connect one API to another, moving information between systems. But these tools aren’t true data fabrics, they work on a case-by-case basis, wiring individual connections between two systems. When you scale to dozens of producers and consumers of data, this point-to-point wiring becomes costly and unmanageable.

The underlying challenge isn’t just that there are many connections to make. It’s that collection formats don’t match consumption needs.

For example, AWS CloudTrail outputs logs wrapped in an envelope that contains multiple records. To use those records in a SIEM or analytics platform like Splunk, each individual event must “stand alone.” That means the envelope’s metadata must be merged into every record, a process known as exploding and reassembling data. Other systems, like Microsoft Defender, follow similar patterns.

So the real problem isn’t only moving data; it’s preparing it: making it clean, enriched, and consumable for the systems that rely on it.

2. Cost Savings

In recent years, data fabrics such as Ingext, Cribl, and CrowdStrike’s Onum have become increasingly popular, not only because they simplify data transformation, but because they reduce cost. When we introduce a data fabric into an environment, the first advantage we gain is control: the ability to decide what data is worth keeping and what can be dropped.

Many systems generate records that add no real analytical value. These might be repetitive warnings, informational messages, or low-level telemetry such as file touches or service heartbeats. They exist for real-time monitoring but don’t improve visibility or understanding once stored. A well-designed data fabric can identify and discard this noise before it reaches downstream systems. In many environments, that means a 40 percent reduction in data volume right away.

The second savings comes from tiered storage. Dense telemetry, the low-value, high-volume data, can be placed in a lower-cost data store, such as a Parquet-based archive, while higher-value events go into performance-oriented systems like a SIEM. This approach speeds up searches, prevents primary systems from being overloaded, and cuts ongoing storage costs. Telemetry data, taken individually, rarely matters; its value appears only when patterns emerge or during an investigation. Cribl believes that there is a 95% reduction of data going into your analysis when you store telemetry in a data lake or metrics database. So, we keep it, just not expensively.

While data fabrics were originally designed to simplify operations and improve data management, their ability to reduce cost and complexity has made them a boardroom-level topic. At the C-suite, conversations about data fabrics often start not with “how it works,” but with “how much it saves.”

What a Data Fabric Does

A data fabric serves as the gateway layer between data collection and data consumption. It doesn’t replace existing systems. It connects them. In doing so, it solves three major technical challenges at once:

• Collection: It supports both push and pull data flows. Some systems forward logs automatically through protocols such as Syslog or HEC, while others require the fabric to pull data from APIs or read directly from storage locations such as S3 buckets.
• Transformation: It converts raw inputs into structured, enriched, and standardized formats so that data from AWS, Microsoft, or any other provider can be interpreted consistently across the environment.
• Routing: It delivers the right data to the right place — whether that’s a SIEM, a data lake, or another operational tool. The same data can even be sent to multiple destinations in different formats, or dropped entirely when it adds no value.

A data fabric is more than a pipe; it’s an intelligent distribution and control layer. It can determine what to keep, what to store cheaply, and what to discard. But perhaps the most overlooked advantage is that it eliminates redundancy across the organization.

Without a data fabric, each department: security, networking, compliance, operations, often builds its own data-collection pipeline. Each repeats the same work of gathering, parsing, and forwarding identical records from the same sources. The result is duplicated effort, inconsistent data handling, and rising infrastructure costs.

With a data fabric, data is collected once, transformed once, and distributed many times. Every team works from the same foundation, and each receives the format they need. This centralization not only improves efficiency but also simplifies accountability. When something breaks, when a data source goes down or a feed stops, there’s one system responsible for detecting and recovering it, rather than three or four different teams discovering the same outage independently.

In this way, the data fabric doesn’t just solve technical problems; it solves organizational ones. It reduces confusion over ownership, prevents wasted duplication of effort, and ensures consistent data quality. The result is another kind of savings, not from storage or compute, but from operations. By consolidating control, the data fabric lowers the human and coordination cost of maintaining complex, interconnected systems.

The Hidden Problems in Moving Data

When data moves between systems, four issues immediately arise: congestion, backflow, distance, and security.

• Congestion happens when the consumer of data, such as a SIEM, database, or analytics tool, can’t process records as quickly as they arrive. When this occurs, the incoming data begins to pile up, causing delays or outright data loss. The problem becomes especially visible during load spikes, when the flow of logs or telemetry suddenly increases. A data fabric handles congestion by queuing and pacing the flow. Instead of overwhelming the consumer, it buffers the excess and releases it steadily, maintaining reliability without dropping information.
• Backflow is the opposite problem. It occurs when the producer sends data faster than the consumer can accept it. In many streaming environments: Syslog, HEC, or API-driven telemetry, the producer doesn’t have an easy way to “pause.” Without a buffering system, data either gets blocked at the source or is lost entirely. A data fabric resolves this by introducing flow control, which balances the rate between sender and receiver. It can absorb bursts of high-volume data and ensure that both sides of the connection remain stable.
• Distance becomes a factor when producers and consumers are in different regions, clouds, or even continents. Long network paths introduce latency and inefficiency, particularly in protocols that expect constant acknowledgment from the receiver. As the distance grows, throughput drops. A data fabric solves this by optimizing transport: compressing, packaging, and relaying data efficiently over long links. It can place intermediate collectors closer to the data source, then move the information securely to its destination. This makes the entire system more responsive and scalable across distributed infrastructures.
• Security is the fourth challenge. Many traditional data protocols, like Syslog over port 514, transmit information in plain text. That means any network device in the path could potentially read or alter the messages. A data fabric enforces secure transport by encrypting and authenticating communications between systems. This ensures that logs, metrics, and telemetry remain protected in transit, even when crossing public networks or cloud boundaries. It eliminates the need for each endpoint to manage its own encryption, simplifying configuration while maintaining compliance.

Together, these four problems: congestion, backflow, distance, and security, define the difference between a simple integration and a true data fabric. A well-designed data fabric doesn’t just connect systems; it keeps data moving smoothly, securely, and reliably, no matter how large or distributed the environment becomes.

The Benefits

Once the basic need and design are clear, the benefits fall naturally into place:

• Reliability: Centralized transformation ensures consistent, predictable data quality. When parsing or enrichment logic changes, it’s applied once and reflected everywhere.
• Ease of Use: Engineers and analysts no longer maintain dozens of local agents or one-off integrations. The data fabric becomes the single control point for how information flows.
• Cost Efficiency: Data is routed to the right place — dense telemetry in low-cost object storage, significant or notable events in high-performance systems like a SIEM.
• Flexibility: Adding or changing a consumer — whether Splunk, Elastic, or a time-series database — requires configuration, not code. This enables rapid adaptation as tools evolve.
• Governance: With a single layer managing data movement, organizations can monitor, audit, and throttle data flows. This control is essential for compliance, privacy, and operational oversight.

At this point, we’ve covered the technical reasons to implement a data fabric. Why it’s not just another integration tool, but an architectural improvement. Yet the value extends beyond engineering. That cleaner design translates into organizational reliability, simplified operations, and better alignment across teams.

In practice, a data fabric reduces confusion about where data comes from, how it’s transformed, and who is responsible for it. It establishes a clear and consistent process for integration, allowing systems to connect and grow without repeatedly reinventing the same solutions. The result isn’t only a more efficient infrastructure, it’s an organization that scales with confidence because its data foundation is stable, governed, and ready to expand.

Conclusion: The Rise of the Data Fabric

Data fabrics are on the rise, and for good reason. Products like Ingext, Cribl, and CrowdStrike Onum demonstrate that organizations are recognizing a shared need: to integrate data more intelligently between sources and consumers. The traditional point-to-point model of collection no longer scales. Every system that produces or consumes data, from network monitoring tools to SIEMs, depends on consistent, timely, and accurate information.

By introducing a data fabric, organizations gain a foundation that makes that consistency possible. They can manage how data is collected, transformed, and routed from a single place, rather than rebuilding integrations over and over. The result isn’t just a cleaner infrastructure. It’s a more capable one. Systems work together instead of competing for the same data. Teams operate with shared visibility and clear lines of responsibility.

The value extends beyond technology. A well-implemented data fabric reduces operational friction, clarifies ownership, and improves how departments communicate. It brings both cost savings and what might be called political savings, fewer turf battles over who owns data, fewer duplicated efforts, and clearer accountability when something goes wrong.

So, take a look at data fabrics. Consider how one could fit within your organization. You may find that it’s the missing layer, the connective tissue that makes your systems not just function, but function together. For many organizations, that realization is the start of a major shift in how data is managed, shared, and valued.