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Unified Observability Solutions

Build comprehensive observability platforms with Prometheus, Grafana, ELK Stack, and OpenTelemetry. Monitor, trace, and analyze your entire infrastructure and applications with unified visibility and intelligent alerting.

Explore Observability Solutions

The Four Pillars of Observability

Metrics

Time-series data that provides quantitative measurements of system performance and behavior

Key Tools:

Prometheus
InfluxDB
Telegraf
StatsD

Examples:

CPU utilization
Memory usage
Request rate
Error count

Typical Retention:

1-5 years

Logging

Structured and unstructured event data that captures what happened in your systems

Key Tools:

Elasticsearch
Logstash
Fluentd
Loki

Examples:

Application logs
System events
Audit trails
Error messages

Typical Retention:

30-90 days

Tracing

Distributed traces that show the journey of requests through microservices architectures

Key Tools:

Jaeger
Zipkin
OpenTelemetry
AWS X-Ray

Examples:

Request flows
Service dependencies
Latency breakdown
Error propagation

Typical Retention:

7-30 days

Events

Discrete occurrences in time that represent changes in system state or user actions

Key Tools:

Fluentd
Vector
Beats
OpenTelemetry

Examples:

Deployments
Scale events
Alerts
User actions

Typical Retention:

90-365 days

Complete Observability Stack

Collection Layer

Agents and collectors that gather telemetry data from applications and infrastructure

OpenTelemetry

Standard

Unified instrumentation framework

Prometheus Node Exporter

Agent

System metrics collection

Filebeat

Agent

Log shipping and forwarding

Jaeger Agent

Agent

Trace collection and batching

Processing Layer

Systems that transform, enrich, and route telemetry data to storage backends

Logstash

Processor

Log processing and transformation

Vector

Processor

High-performance data pipeline

OpenTelemetry Collector

Processor

Telemetry data processing

Telegraf

Processor

Metrics processing and routing

Storage Layer

Databases and time-series stores optimized for different types of observability data

Prometheus

TSDB

Metrics storage and querying

Elasticsearch

Search Engine

Log storage and search

Jaeger Backend

Trace DB

Trace storage and retrieval

InfluxDB

TSDB

Time-series data storage

Visualization Layer

Dashboards and interfaces for exploring and analyzing observability data

Grafana

Dashboard

Metrics visualization and dashboards

Kibana

Analytics

Log exploration and analytics

Jaeger UI

Trace UI

Trace visualization and analysis

Chronograf

Dashboard

InfluxDB data visualization

Proven Monitoring Patterns

RED Method

Monitor Request rate, Error rate, and Duration for service-oriented architectures

Key Metrics:

  • Requests per second
  • Error percentage
  • Response time percentiles

Recommended Tools:

Prometheus
Istio
Envoy

Best For:

Microservices and web applications

USE Method

Monitor Utilization, Saturation, and Errors for infrastructure resources

Key Metrics:

  • CPU/Memory utilization
  • Queue lengths
  • Error counts

Recommended Tools:

Prometheus
Node Exporter
Telegraf

Best For:

Infrastructure and system monitoring

Four Golden Signals

Google's approach focusing on Latency, Traffic, Errors, and Saturation

Key Metrics:

  • Request latency
  • Request rate
  • Error rate
  • System saturation

Recommended Tools:

Prometheus
Grafana
OpenTelemetry

Best For:

Large-scale distributed systems

SLI/SLO Monitoring

Service Level Indicators and Objectives for reliability engineering

Key Metrics:

  • Availability
  • Latency percentiles
  • Error budget burn rate

Recommended Tools:

Prometheus
Grafana
SLO generators

Best For:

Production systems with reliability requirements

Troubleshooting Workflows

High Latency Investigation

Systematic approach to investigating and resolving performance issues

Investigation Steps:

  1. Identify affected services using service maps
  2. Check RED metrics for bottleneck services
  3. Analyze distributed traces for slow operations
  4. Correlate with infrastructure metrics
  5. Review application logs for errors
  6. Implement fixes and monitor improvement

Tools Used:

Grafana dashboards
Jaeger traces
Elasticsearch logs
Service topology

Expected Resolution Time:

15-30 minutes

Error Rate Spike

Quick identification and resolution of error rate increases

Investigation Steps:

  1. Identify error patterns in logs
  2. Check recent deployments and changes
  3. Analyze error distribution across services
  4. Review relevant traces for error context
  5. Implement rollback or hotfix
  6. Monitor error rate recovery

Tools Used:

Kibana error dashboards
Deployment tracking
Distributed traces

Expected Resolution Time:

10-20 minutes

Service Outage

Complete service failure investigation and recovery process

Investigation Steps:

  1. Confirm service health status
  2. Check infrastructure availability
  3. Review deployment and configuration changes
  4. Analyze dependency failures
  5. Implement emergency procedures
  6. Conduct post-incident review

Tools Used:

Health check dashboards
Infrastructure monitoring
Change tracking

Expected Resolution Time:

5-15 minutes for detection

Resource Exhaustion

Investigating and resolving resource capacity issues

Investigation Steps:

  1. Identify resource utilization patterns
  2. Check for memory leaks or CPU spikes
  3. Analyze historical trends and capacity
  4. Review auto-scaling configurations
  5. Scale resources or optimize code
  6. Implement preventive measures

Tools Used:

Infrastructure dashboards
Resource utilization metrics
Trend analysis

Expected Resolution Time:

20-45 minutes

Observability Cost Optimization

Data Retention

Optimize storage costs through intelligent data lifecycle management

Optimization Strategies:

  • Implement tiered retention policies
  • Use data compression and downsampling
  • Archive old data to cheaper storage
  • Delete irrelevant or duplicate data
  • Implement log sampling for high-volume streams

Tools & Technologies:

Elasticsearch ILM
Prometheus recording rules
S3 lifecycle policies

Potential Savings:

Up to 70% storage cost reduction

Efficient Querying

Reduce compute costs through optimized query patterns and indexing

Optimization Strategies:

  • Use time-based partitioning
  • Implement proper indexing strategies
  • Cache frequent queries
  • Use materialized views and rollups
  • Optimize PromQL and KQL queries

Tools & Technologies:

Query performance analyzers
Index optimizers
Caching layers

Potential Savings:

Up to 50% query cost reduction

Infrastructure Right-Sizing

Optimize infrastructure costs based on actual usage patterns

Optimization Strategies:

  • Monitor resource utilization patterns
  • Implement auto-scaling policies
  • Use spot instances for non-critical workloads
  • Consolidate underutilized services
  • Optimize network data transfer

Tools & Technologies:

Cloud cost analyzers
Resource monitoring
Auto-scaling tools

Potential Savings:

Up to 40% infrastructure cost reduction

Smart Sampling

Reduce data volume while maintaining observability quality

Optimization Strategies:

  • Implement intelligent trace sampling
  • Use head-based and tail-based sampling
  • Apply log level filtering
  • Implement metric cardinality control
  • Use probabilistic data structures

Tools & Technologies:

OpenTelemetry sampling
Jaeger adaptive sampling
Log filters

Potential Savings:

Up to 80% data volume reduction

Ready to Build Unified Observability?

Transform your monitoring and troubleshooting capabilities with a comprehensive observability platform. Our experts will design and implement solutions that provide complete visibility into your systems.

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