Cribl
Data engineering and observability pipeline vendor
Cribl is a company that builds software for routing, shaping, and reducing machine data such as logs, metrics, and traces before it reaches a destination like a SIEM, observability platform, or data lake. Its flagship product, Cribl…
Definition
Cribl is a company that builds software for routing, shaping, and reducing machine data such as logs, metrics, and traces before it reaches a destination like a SIEM, observability platform, or data lake. Its flagship product, Cribl Stream, sits between data sources and downstream tools so teams can filter noise, mask sensitive fields, and control storage costs without changing what applications emit. The company positions its products as a vendor-neutral control layer for telemetry data.
Overview
Organizations that run security and observability programs typically ingest data from dozens of sources: application logs, cloud provider events, network telemetry, and infrastructure metrics. Sending all of it unfiltered into a SIEM or observability backend is expensive, because most of these platforms charge by data volume, and much of what is collected is redundant or low value for detection and troubleshooting. Cribl addresses this by inserting a processing layer that inspects data in flight and only forwards what downstream tools actually need. Mechanically, Cribl Stream works as a pipeline of sources, processing pipelines, and destinations. Data enters through a source connector (an agent, syslog listener, cloud API, or message queue), passes through a pipeline of functions that can parse, enrich, redact, sample, or drop events, and then routes to one or more destinations based on rules. This lets a security team send full-fidelity data to cheap object storage for retention while forwarding only high-signal, normalized events to an expensive SIEM, a pattern the company calls the "observability pipeline" model. Cribl differs from a general-purpose stream processor like Apache Kafka or a log shipper like Fluentd or Logstash in that it is purpose-built for the specific vendors and schemas used in security and IT operations, with out-of-the-box connectors for tools such as Splunk, Elasticsearch, and cloud logging services. It is not a SIEM or an analytics platform itself; it is middleware that makes existing SIEM and observability investments more efficient rather than replacing them. Where a raw shipper simply moves bytes from one place to another, Cribl's pipeline stage understands the shape of the data well enough to drop noisy fields, collapse duplicate events, and reroute a single stream to several destinations at once based on content rather than just source. In practice, security and platform engineering teams deploy Cribl Stream as a fleet of worker nodes, often managed through a central leader node, to normalize formats across log sources, strip personally identifiable information before it lands in a shared analytics tool, and re-route data during vendor migrations without re-instrumenting every application. Cribl also ships Cribl Edge for lightweight collection at the source and Cribl Search for querying data where it rests rather than moving it first. The trade-off is added architectural complexity: introducing a pipeline layer means another system to operate, secure, and monitor, and transformation rules written incorrectly can silently drop or corrupt data before anyone notices. Teams with a single, simple data source and a generous SIEM budget may not need it. It is best suited to organizations already paying real money for ingestion volume and juggling more than one downstream analytics tool. Getting the most out of it also requires someone on the team to actually own pipeline design over time, since data sources, schemas, and destination vendors all change, and a pipeline configured once and left alone tends to drift out of sync with what the business actually needs to retain or forward.
Key Features
- Routes and transforms machine data in flight before it reaches a destination
- Reduces SIEM and observability ingestion costs through filtering and sampling
- Ships prebuilt connectors for common log, metric, and cloud sources
- Supports field-level masking and redaction of sensitive data
- Cribl Edge collects data directly on hosts with a lightweight agent
- Cribl Search queries data in place without moving it first
- Vendor-neutral design lets teams change SIEM or storage backends independently
- Central leader node manages configuration across distributed worker fleets