World Congress 2025 Aug 20, 2025 Session details

Logs in observability - Correlation

Michal Bojko

Stop treating logs as an afterthought. By adopting log-driven development and K-means clustering, you can transform petabytes of noisy data into actionable business intelligence within seconds.

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#1 about 4 min

Understanding the daily challenges of massive log volumes

Analyzing gigabytes of chaotic infrastructure logs presents a significant scaling challenge without proper data storytelling.

#2 about 5 min

Creating contextual data stories from messy log files

Adding identifiers and descriptive values during logging enables teams to trace user flows across disparate systems.

#3 about 3 min

Automating log correlation through preprocessing and clustering

Parsing varying timestamp formats and establishing time constraints allows algorithms to cluster discrete system events efficiently.

#4 about 4 min

Applying K-means clustering to group massive datasets

The K-means algorithm translates system metrics into numeric values to efficiently group parameters like high CPU usage across millions of logs.

#5 about 3 min

Choosing between rule-based queries and machine learning

While exact filtering works for known parameters, non-deterministic log structures require dynamic algorithms to reveal hidden infrastructure relationships.

#6 about 4 min

Transitioning from monitoring to proactive system observability

Rather than just spotting immediate failures, true observability analyzes historical data patterns to predict issues and measure overarching business efficiency.

#7 about 3 min

Implementing log-driven development for better software reporting

Designing clear log messages and categorization hierarchies before writing code ensures the application generates intuitive, actionable system insights.

Matching moments

2:41 min

Defining observability beyond basic metrics, logs, and traces

Mathias Palmersheim Mathias Palmersheim · Europe 2026 Virtual

2:29 min

Moving beyond logging to comprehensive API observability

Rustam Mehmandarov Rustam Mehmandarov · WWC Europe 2026

17:03 min

Introduction to metrics and observability challenges in monitoring

Liam Hurrell · LIVE

2:12 min

Elevating observability with anomaly detection and root cause analysis

Raz Cohen · LIVE

1:30 min

Processing and structuring noisy application logs for LLMs

Alisa Hrustic Alisa Hrustic · Europe 2026 Virtual

3:57 min

Analyzing log generation scale in Kubernetes microservice architectures

Aliaksandr Valialkin Aliaksandr Valialkin · WWC Europe 2026

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