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Telemetry pipeline control

Cribl development for teams that need control over their telemetry

We help teams route, shape, enrich, redact, archive, and replay observability data before it becomes noisy, expensive, risky, or trapped in one destination.

Routing, filtering, enrichment, and redaction
Stream, Edge, replay, archive, and destination strategy
Governed pipelines your team can maintain
Talk through a telemetry problem

Telemetry control plane

Route the right data to the right place

Route

By value

Shape

Before ingest

Replay

When needed

Routing plan

Governed
Endpoint logsFiltered

SIEM + archive

Cloud eventsEnriched

Splunk + storage

App telemetrySampled

Observability stack

Route by use case
Reduce waste safely
Preserve replay paths
Why it matters

Observability data needs a control plane before it needs another destination.

Modern environments generate more telemetry than teams can afford to search, store, or understand. Without a routing strategy, every destination becomes a compromise between cost, visibility, retention, and risk.

Cribl gives teams a place to make those decisions deliberately: what to keep hot, what to archive, what to redact, what to enrich, and what to send somewhere else.

Control where data goes

Route telemetry to Splunk, object storage, SIEMs, data lakes, observability tools, and archive destinations based on value and use case.

Shape data before it lands

Parse, enrich, redact, sample, suppress, and normalize data upstream so downstream platforms receive cleaner signal.

Reduce waste without losing evidence

Separate high-value searchable data from archive, replay, and lower-cost retention paths without blind deletion.

Make telemetry governable

Create reusable pipeline patterns, ownership, documentation, and change control around observability data flows.

What we build

Cribl pipelines with owners, rules, and purpose

We design telemetry paths so teams know why data moves, where it lands, what changed, and how to safely modify it.

Pipeline architecture

Design Cribl as the control plane between sources, destinations, retention tiers, and operational workflows.

  • Source and destination inventory for security, observability, compliance, and archive use cases
  • Route design for Splunk, object storage, Elastic, Datadog, SIEMs, data lakes, and custom endpoints
  • Environment patterns for dev, test, production, versioning, rollback, and change approval
Filtering, enrichment, and redaction

Transform telemetry before it becomes expensive, noisy, risky, or hard to use.

  • Field extraction, normalization, sampling, suppression, masking, and enrichment logic
  • PII, secret, and sensitive-data handling before data reaches downstream tools
  • Custom functions and pipelines for source-specific transformation requirements
Replay and retention strategy

Keep access to historical evidence without sending every byte to the most expensive destination.

  • Hot, searchable, archive, replay, and compliance retention patterns
  • Object storage and low-cost destination strategies for investigation backfill
  • Replay-ready pipeline design so teams can recover missed data when needed
Operational handoff

Make Cribl understandable and safe for the teams that will maintain it after implementation.

  • Pipeline documentation, ownership, naming conventions, and review cadence
  • Testing and validation patterns for transformations, routes, and destination behavior
  • Training for operations, security, data engineering, and platform teams
Deliverables

A telemetry pipeline your team can reason about.

Cribl work should leave behind routes, transformations, validation, and documentation that survive beyond the first deployment.

Telemetry source, destination, and routing architecture
Cribl Stream and Edge pipeline implementation
Filtering, sampling, redaction, enrichment, and normalization logic
Replay, archive, and retention strategy
Validation checks for data completeness, fidelity, and destination behavior
Documentation, training, and maintainable pipeline ownership model
Our point of view

Filtering data is easy. Governing telemetry is the real work.

Teams need to know what changed, who approved it, what evidence was preserved, and whether downstream tools still receive the signal they depend on.

Every route should have a business, security, or operational reason.
Data reduction should not silently destroy evidence teams may need later.
Redaction and masking should happen before sensitive fields spread downstream.
Replay paths should exist before an investigation needs historical data.
Pipeline changes should be testable, reviewable, and documented.
Process

How we approach Cribl development

We start with the data decision, then design the pipeline around value, risk, and downstream use.

1
Map the flows

We inventory data sources, destinations, volumes, costs, retention needs, security constraints, and users.

2
Design routes

We define what should be searchable, archived, replayable, filtered, enriched, redacted, or sent elsewhere.

3
Build pipelines

We implement Stream, Edge, custom functions, transformations, routes, and destination-specific behavior.

4
Validate and hand off

We test fidelity, performance, and outcomes, then document patterns so your team can safely operate Cribl.

Common questions

Cribl development FAQ

Straight answers for teams building a more intentional observability pipeline.

Related services

Cribl is strongest when routing strategy connects to search, security operations, and custom workflows.

Splunk Development

Build searches, detections, dashboards, and reporting on top of clean, routed telemetry.

Learn more
AI-Native Software Development

Build custom workflow tools around telemetry routing, alerting, investigation, and reporting.

Learn more
Continuous Threat Exposure Management

Turn external exposure and remediation findings into operational security signal.

Learn more
Talk through a telemetry problem
Expert Security Solutions

Take control of your observability pipeline

Tell us where telemetry is creating pain: runaway ingest, duplicated data, sensitive fields, destination sprawl, missing replay paths, or Splunk cost pressure.

Schedule a Free Consultation
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