EEdukron DevOps Library
ObservabilityIntermediate10 submodules

Centralized Logging and Analysis

Collect application, operating-system, container, Kubernetes, and Azure logs into centralized platforms with parsing, correlation, querying, retention, access, and cost controls.

How this module works

From concept to production evidence

Emit structured events with timestamps and correlation IDs, collect close to the source, enrich consistently, protect sensitive fields, route to appropriate stores, and set retention by operational and compliance value.

Learning outcomes

  • Differentiate log sources
  • Design collection and enrichment
  • Query Azure logs with KQL
  • Explain ELK architecture
  • Control retention, access, cost, and sensitive data

Flow diagram

Centralized Logging and Analysis end-to-end flow

Use this sequence as the operating backbone for every submodule note and hands-on exercise.

  1. 01
    Emit eventCentralized Logging and Analysis stage 1: emit event with an auditable result before the next stage begins.
  2. 02
    Collect and bufferCentralized Logging and Analysis stage 2: collect and buffer with an auditable result before the next stage begins.
  3. 03
    Parse and enrichCentralized Logging and Analysis stage 3: parse and enrich with an auditable result before the next stage begins.
  4. 04
    Route and indexCentralized Logging and Analysis stage 4: route and index with an auditable result before the next stage begins.
  5. 05
    Query and correlateCentralized Logging and Analysis stage 5: query and correlate with an auditable result before the next stage begins.
  6. 06
    Archive or expireCentralized Logging and Analysis stage 6: archive or expire with an auditable result before the next stage begins.

Integrated hands-on lab

Centralize a multi-platform log journey

  1. Define structured log schema
  2. Collect host, container, and Kubernetes logs
  3. Route Azure diagnostics
  4. Create KQL or ELK queries
  5. Apply masking and retention policy

Complete module contents

10 full submodule notes

Read in order
01

Application logs

Application logs turns service behavior into evidence that supports detection, diagnosis, capacity decisions, and measurable reliability within Centralized Logging and Analysis. It is not an isolated definition: its value appears when it supports emit structured events with timestamps and correlation IDs, collect close to the source, enrich consistently, protect sensitive fields, route to appropriate stores, and set retention by operational and compliance value.

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02

System logs

System logs turns service behavior into evidence that supports detection, diagnosis, capacity decisions, and measurable reliability within Centralized Logging and Analysis. It is not an isolated definition: its value appears when it supports emit structured events with timestamps and correlation IDs, collect close to the source, enrich consistently, protect sensitive fields, route to appropriate stores, and set retention by operational and compliance value.

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03

Container logs

Container logs turns service behavior into evidence that supports detection, diagnosis, capacity decisions, and measurable reliability within Centralized Logging and Analysis. It is not an isolated definition: its value appears when it supports emit structured events with timestamps and correlation IDs, collect close to the source, enrich consistently, protect sensitive fields, route to appropriate stores, and set retention by operational and compliance value.

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04

Kubernetes logs

Kubernetes logs turns service behavior into evidence that supports detection, diagnosis, capacity decisions, and measurable reliability within Centralized Logging and Analysis. It is not an isolated definition: its value appears when it supports emit structured events with timestamps and correlation IDs, collect close to the source, enrich consistently, protect sensitive fields, route to appropriate stores, and set retention by operational and compliance value.

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05

Azure logs

Azure logs turns service behavior into evidence that supports detection, diagnosis, capacity decisions, and measurable reliability within Centralized Logging and Analysis. It is not an isolated definition: its value appears when it supports emit structured events with timestamps and correlation IDs, collect close to the source, enrich consistently, protect sensitive fields, route to appropriate stores, and set retention by operational and compliance value.

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06

Log Analytics

Log Analytics turns service behavior into evidence that supports detection, diagnosis, capacity decisions, and measurable reliability within Centralized Logging and Analysis. It is not an isolated definition: its value appears when it supports emit structured events with timestamps and correlation IDs, collect close to the source, enrich consistently, protect sensitive fields, route to appropriate stores, and set retention by operational and compliance value.

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07

KQL

KQL turns service behavior into evidence that supports detection, diagnosis, capacity decisions, and measurable reliability within Centralized Logging and Analysis. It is not an isolated definition: its value appears when it supports emit structured events with timestamps and correlation IDs, collect close to the source, enrich consistently, protect sensitive fields, route to appropriate stores, and set retention by operational and compliance value.

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08

Centralized logging

Centralized logging turns service behavior into evidence that supports detection, diagnosis, capacity decisions, and measurable reliability within Centralized Logging and Analysis. It is not an isolated definition: its value appears when it supports emit structured events with timestamps and correlation IDs, collect close to the source, enrich consistently, protect sensitive fields, route to appropriate stores, and set retention by operational and compliance value.

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09

ELK Stack

ELK Stack provides a concrete platform capability that must be configured, secured, validated, observed, and owned throughout its lifecycle within Centralized Logging and Analysis. It is not an isolated definition: its value appears when it supports emit structured events with timestamps and correlation IDs, collect close to the source, enrich consistently, protect sensitive fields, route to appropriate stores, and set retention by operational and compliance value.

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10

Log retention

Log retention turns service behavior into evidence that supports detection, diagnosis, capacity decisions, and measurable reliability within Centralized Logging and Analysis. It is not an isolated definition: its value appears when it supports emit structured events with timestamps and correlation IDs, collect close to the source, enrich consistently, protect sensitive fields, route to appropriate stores, and set retention by operational and compliance value.

Open full notes