# 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

## 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.

- 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.

- **What does Cribl development include?**  
- **How is Cribl different from Splunk?**  
- **Can Cribl reduce observability costs?**  
- **Do you work with Cribl Edge?**  
- **Can Cribl help with security and compliance data?**  
- **Will our team be able to maintain the pipelines?**

## 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.