Cross-service correlation
Connect related events across your entire stack, in the pipeline itself, before they reach your observability platform.
Unlimit your data management
Full telemetry pipeline
Go beyond simple transformations with real-time analytics legacy pipelines can’t handle.
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Legacy pipelines are stateless,
limiting them to routing and transformations.
The consequence
Each event is processed independently, with no memory of what happened a moment ago or on another node. That design scales easily for simple filtering, but it creates architectural limitations on what the pipeline can do.
No matter how many rules you write, legacy pipelines can't:
Connect related events across services.
Power alerts that depend on tracking behavior over time.
Build real-time analytics on user behavior across services.
Understand the whole transaction, not just one event
Grepr empowers you to analyze complete transactions across your entire stack in real time.
Cross-service correlation
Stateful streaming
Stateful streaming
Detect multistage patterns and build alerts that adapt to behavior, not just thresholds.
- Ex: Detect a failing login ten or more times before succeeding and then accessing a sensitive service.
- Ex: Flag that ten percent of users abandoned a purchase after adding an item to cart.
Streaming joins
Streaming joins
Link events together through sessionization to capture an entire transaction as it happens, then build real-time analytics on top to understand the behavior across your entire system.
SQL transformations
SQL transformations
Reshape and enrich your data in stream, or feed clean, correlated data straight into your AI workflows.
- Ex: Convert a nested list into multiple logs.
- Ex: Drop or transform fields in your logs.
- Ex: Extract structured data or mask sensitive data.
- Ex: Transform the shape of a log message to align it with OCSF.
Aggregations
Aggregations
Grepr performs aggregations across everything you send it, with powerful scalable groupings, like emitting a real-time count metric for every occurrence of a log with specific features, doing 'streaming Top-N Lists'.
Cross-service correlation
Connect related events across your entire stack, in the pipeline itself, before they reach your observability platform.
Stateful streaming
Detect multistage patterns and build alerts that adapt to behavior, not just thresholds.
- Ex: Detect a failing login ten or more times before succeeding and then accessing a sensitive service.
- Ex: Flag that ten percent of users abandoned a purchase after adding an item to cart.
Streaming joins
Link events together through sessionization to capture an entire transaction as it happens, then build real-time analytics on top to understand the behavior across your entire system.
SQL transformations
Reshape and enrich your data in stream, or feed clean, correlated data straight into your AI workflows.
- Ex: Convert a nested list into multiple logs.
- Ex: Drop or transform fields in your logs.
- Ex: Extract structured data or mask sensitive data.
- Ex: Transform the shape of a log message to align it with OCSF.
Aggregations
Grepr performs aggregations across everything you send it, with powerful scalable groupings, like emitting a real-time count metric for every occurrence of a log with specific features, doing 'streaming Top-N Lists'.
FAQs
What is telemetry management?
Telemetry management is the process of controlling how logs, metrics, traces, and events are collected, routed, filtered, stored, and sent to observability tools. Good telemetry management helps teams keep useful visibility while reducing noisy or repetitive data that drives unnecessary cost and operational work.
Why is telemetry management difficult for modern engineering teams?
Telemetry management becomes difficult as teams add more services, releases, environments, and observability data sources. Manual rules, filters, and routing decisions can quickly become hard to maintain. Teams need enough data for reliability and incident response, but sending every low-signal event into premium observability tools can create high costs and noisy workflows.
How does Grepr automate telemetry management?
Grepr automates telemetry management by identifying repetitive and low-signal telemetry patterns before they reach downstream observability platforms. Grepr forwards useful signal and summaries to the tools teams already use while preserving raw telemetry in lower-cost storage for incidents, investigations, and historical context.
What is a Grepr processing job?
A Grepr processing job is a unit of telemetry work. Jobs can run in streaming mode for live telemetry or batch mode for files, historical data, data lake queries, and backfills. Jobs can include operations such as reducers, filters, routes, sinks, and SQL transforms.
Why does batch testing matter for telemetry automation?
Batch testing lets teams validate processing behavior on files or historical data before applying the same logic to live telemetry streams. That helps teams reduce rollout risk when changing how telemetry is filtered, routed, summarized, or transformed.
Does automated telemetry management mean losing raw data?
No. Grepr should be positioned as reducing noisy downstream volume while preserving raw telemetry elsewhere. This gives teams a safer alternative to simply dropping logs, sampling aggressively, or shortening retention when observability costs rise.
How does Grepr help protect dashboards and alerts?
Grepr’s query-aware capabilities can parse dashboards and alerts so important telemetry paths can be protected from reduction. Final public copy should list only confirmed integrations and supported query languages.
Does Grepr replace Datadog, Splunk, New Relic, or Grafana Cloud?
No. Grepr is designed to work with existing observability platforms. The goal is to reduce noisy telemetry before it becomes expensive ingest, indexing, or storage while keeping the observability workflows teams already rely on.
How is Grepr different from manual telemetry pipeline rules?
Manual telemetry pipeline rules require teams to decide in advance what should be routed, filtered, transformed, or dropped. Grepr’s pattern-detection approach is intended to identify repetitive telemetry as systems run, reducing the ongoing rule-maintenance burden.
Who owns telemetry management in an engineering organization?
Telemetry management is often shared across platform engineering, SRE, observability, DevOps, and FinOps teams. Engineering leaders care because telemetry affects reliability, cost, developer workflows, and incident response. Grepr’s page should speak to all of these stakeholders without making the message too generic.
What is the safest way to automate telemetry management?
The safest approach is to reduce low-value downstream telemetry volume without permanently losing raw context. That means preserving raw data, keeping existing observability tools in place, testing changes before live rollout, and giving teams access to fuller detail when incidents or investigations require it.
Ready to upgrade pipelines?
See how Grepr unlocks entirely new use cases for your team.