Overview
Security and observability teams evaluating Cribl are often focused on controlling data movement, optimizing storage costs, and building scalable pipelines. Cribl does this well. Projects often begin from a desire to reduce SIEM costs. But for security teams who need fast answers—not just better routing—Query solves a different problem.
Cribl is helpful for managing what data reaches tools like Splunk and SIEMs, especially for high-volume sources, such as EDR. But building those pipelines takes time, planning, and often external support. Security relevant data is highly distributed across modern environments. Efficient and effective security operations require accessing and using more of your data when you need it.
Last week, we published a piece about how Query & Cribl are better together. This blog breaks down what each product is uniquely suited for and where to use each one to solve specific challenges, depending whether your focus is data mobility & management or using data in security operations workflows.
What Cribl Does
Cribl is a data mobility tool that provides various mechanisms to move data from source systems into prebuilt destinations, or natively in the Cribl platform via Cribl Lake or Cribl Lakehouse. Cribl Edge is the product used to move data from hosts, such as Windows Event Logs, Syslog, Kubernetes control planes logs, and more. Cribl Stream is the product used to pull from data sources, or have data pushed from the sources to Cribl. Stream supports data from HTTP, TCP, SNMP, and Syslog servers and endpoints as well as direct sources such as AWS Kinesis, Kafka, and otherwise.
Within Edge and Stream you can configure pre-processing and post-processing rules and apply Cribl Packs (templates) to the data to provide light transformation, disaggregation, and normalization. Advanced users can provide full Extraction, Transformation, and Loading (ETL) functionality to their Edge and Stream pipelines to land disaggregated, normalized, and standardized data with efficient formats, compression, and partitioning.
From a security team’s perspective, Edge and Stream are typically used to remove data from legacy SIEMs and on-premise systems and move the data into more cost-effective storage such as cloud-based object storage or dedicated data intelligence platforms such as Databricks or Snowflake. Being a platform-as-a-service (PaaS) tool, Cribl offers nearly endless functionality for this mission, but without deep domain expertise can also introduce several antipatterns into data storage and optimization. Nearly every source will require its own processing rules and data templates, as well as its own heavy operational burden to ensure that data volumes and outages are carefully managed.
Cribl’s primary use case is reducing the volume (and therefore the cost) of data sent into Splunk, and other select destinations. It does this by preprocessing, filtering, and routing data before it reaches the target platform. While effective for cost management, this approach requires significant upfront effort to implement. Teams must define what data to keep, transform, or drop, and build pipelines accordingly, often involving engineering support and professional services. This prework can delay access to valuable data and slow response to new investigation or compliance requirements.
What Query Does
Query is a Federated Search & Analytics platform for security operations that creates an OCSF normalized data mesh of all your security relevant data. It connects directly to security, IT, cloud/SaaS systems, and existing data lakes, warehouses, and SIEMs via API and allows analysts to use all of them in real time. No data movement. No delay. No engineering required. Security teams use Query directly, through our UI or APIs, or by integrating us into Splunk or their Agentic Systems and AI workflows.
Query is purpose-built for security teams to use data more completely to improve investigations, threat hunting, and response. It delivers data driven answers using more of your security-relevant data sources, without requiring data to be moved, duplicated, transformed or pipelined first. Analysts can access, search, and get answers across systems without needing to know each data source’s structure or query language. Results are returned as a single, unified dataset normalized to the Open Cybersecurity Schema Framework (OCSF) regardless of the original format, so teams can immediately correlate, filter, and act.
Building a Security Data Mesh, powered by AI driven Federated Search and Analytics, Query delivers data located anywhere. You already have the security data. Now both humans and systems can use it to improve security operations using your existing tech stack, tools, and data storage choices. Use Query Copilot and Agents, tailored to specific security missions, or leverage the Query Data Mesh as a gateway to build your own Agentic Systems and workflows. Query integrates with Cribl Search. And if you are using Splunk, Query plugs right in as an app, unlocking the full data reach of Query from inside Splunk.
Query includes a native Splunk app that extends the reach of Splunk to include distributed data sources that would otherwise be cost-prohibitive to ingest. This extends the Query OCSF normalized API gateway into Splunk, allowing teams to continue using familiar SPL and dashboards, while unlocking broader context and coverage—without increasing storage volume or licensing costs.
Key Differences at a Glance
Features |
 |
 |
| Primary Function | Data routing and pipeline management | Federated search and investigation across distributed data |
| Requires Centralization | Yes, to final destination (e.g. SIEM, lake) | No, data stays at the source |
| Security Analyst Focus | Indirect | Direct, built for the SOC workflow |
| Time to Insight | Delayed (post-routing and indexing) | Real-time |
| Pivots Required | Yes, across tools after data lands | No, unified search across systems in one console |
| Enrichment Capabilities | Partial, requires customization and depends source-to-source | Yes, utilize Federated Joins to automatically enrich records with data from other records |
| Normalization & Standardization | Partial, requires customization or limited Packs | Yes, all results are automatically normalized just-in-time at search |
| API Filtering | No, filtering is only available via post-processing | Yes, request specific data from specific endpoints in source systems (e.g., get vulnerabilities only) |
| Use Case Fit | Optimizing data engineering workflows | Accelerating investigations, hunts, and response |
| Direct Splunk Integration | No, only through data pipelines | Yes, OCSF-normalized API gateway with native Splunk app |
Summary
Cribl optimizes how data moves. Query optimizes how people use data, allowing security teams to reduce time-to-answer, improve analyst efficiency, and leverage more of the data they already have.
