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Cross Cloud Observability Pipelines

Written by Ashnik Team

Unified Observability Solutions | Jul 08, 2025

3 min read

Cross-Cloud Observability Pipelines: Ingest What Matters

Are your observability pipelines overflowing with data—but lacking insight?

In almost every architecture review I’ve done this year—from Kubernetes-heavy banking stacks to telco-grade event pipelines—I’ve found the same issue: tons of data, little clarity.

This post shows you how to design high-signal Cross-Cloud Observability Pipelines. You’ll learn what to ingest (and what to drop), how to architect across cloud providers, and which optimizations deliver speed, savings, and SLO clarity.

Why Cross-Cloud Observability Is No Longer Optional

Modern infrastructure lives across:

  • AWS EKS clusters
  • Azure AKS microservices
  • GCP Cloud Functions
  • SaaS APIs + third-party SDKs

This fragmentation breaks unified tracing, bloats costs, and creates operational blind spots.

A well-architected observability pipeline stitches this together, ensuring:

  • Actionable telemetry across services
  • Aligned SLOs, alerting, and RCA across teams
  • Cost control through selective ingestion

It’s the glue layer between raw data and decision-making.

What to Ingest—and Why

Data Type Ingest Why It Matters
Logs WARN/ERROR+, app logs with context / CSV Troubleshooting, RCA
Metrics RED metrics, infra SLO signals Health checks, alert thresholds
Traces Full traces with errors, latency Distributed RCA, performance profiling
Events Deploy, restart, scaling, failures Change tracking, timeline correlation

The Pipeline Blueprint (Across Clouds)

Here’s my field-tested 4-layer pattern, adaptable to any multi-cloud setup.

Collection

Transport

  • Kafka / MSK / PubSub
  • Topic separation by app/env/cloud
  • Buffer for retries + schema validation (Avro, JSON)

Processing

  • Logstash Vector.dev
  • ECS field mapping + custom grok patterns
  • Tail-based log and trace sampling

Storage + Query

  • Elastic ILMwith hot/warm/cold tiers
  • Searchable snapshots in S3/GCS
  • Unified dashboards in Kibana, Grafana
  • Optimized ingest enables Elastic ML anomaly detection, helping surface latent issues via unsupervised models (e.g., latency spikes, error bursts)

Sample Format:

cross

[Fluent Bit@Azure] --> Kafka --> Logstash (drop, enrich) --> Elastic Cloud
[Otel Collector@AWS] --> Kafka --> Vector (tail sample) --> Grafana Tempo

Smart Ingest Practices

Tail-Based Sampling (Traces):

Save only spans with errors, slow latencies, or business impact. Use Otel’s tail sampling policies to:

processors:
tail_sampling:
decision_wait: 10s
policies:
- type: status_code
status_codes: [ERROR]
- type: probabilistic
sampling_percentage: 20

Log Drop Filters (Edge):

[FILTER]
Name grep
Match *
Exclude log.level debug

Index Lifecycle Management (ILM):

Hot: 7d → Warm: 30d → Cold: snapshot in S3

Guide: Elastic ILM Tutorial

Route by Cloud

Use dynamic index patterns like:

logs-aws-app-prod-*
logs-gcp-app-staging-*

Common Pitfalls (And Fixes)

Pitfall Solution
Over-collecting low-value logs Filter early at edge (Filebeat/Fluent Bit)
Unlinked spans in tracing Use consistent trace.id from Otel SDKs
Expensive hot storage Use searchable snapshots for compliance archives
Tool sprawl per cloud Standardize on ECS, shared pipeline config

Real-World Impact: From Chaos to Clarity

Case: Indian Small Finance Bank (IBMB App)

  • Ingested: 1.5B events/day
  • Stack: Kubernetes on AWS, Elastic Cloud on Azure
  • Problems: Disjointed metrics, trace-log disconnect, alert fatigue

What We Did:

  • Kafka as central bus
  • Unified Logstash processors per region
  • ECS alignment + trace ID correlation
  • ILM for warm+archive storage

Outcome:

  • 3x faster RCA
  • 35% reduction in ingest volume
  • 50% fewer false-positive alerts

Read full case study → Real-Time Observability for Kubernetes Banking App

Log-Metric-Trace Correlation in Action

Your observability pipeline should enable multi-dimensional RCA. Here’s how I set it up using Elastic:

  1. APM trace triggers investigation
    High latency on /checkout traced to payment-service
  2. Log correlation kicks in
    In Kibana → APM → Error view → Linked log tab shows:

    {
    "log.level": "error",
    "trace.id": "a1b2c3",
    "message": "NullReferenceException",
    "user.id": "10213"
    }
  3. Metric overlay confirms infra issues
    Metricbeat dashboard shows spike in heap usage & GC time

All from a single click in Kibana using trace.id, span.id, and ECS-mapped fields

Learn how Elastic APM connects logs + metrics

Final Take: Ingest with Intention

Cross-cloud observability isn’t about “collect everything.” It’s about ingesting what matters, with context, clarity, and cost-control built in.

The best pipelines act like smart APIs:

  • Filter at source
  • Enrich with context
  • Route intelligently
  • Store for fast queries + deep forensics

Need help re-architecting your observability stack?

Talk to Ashnik’s Elastic specialists to design a cross-cloud observability pipeline that delivers clarity, not chaos.

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