The Telemetry Blueprint Hyperscale Data Lakes and Machine Learning for Infrastructure Intelligence
Beschreibung
Modern digital infrastructure-from hyperscale data centers to global cloud networks-generates an unprecedented volume of telemetry data. This data holds the key to understanding system behavior, predicting failures, and enabling intelligent, autonomous operations. This book provides a comprehensive guide to infrastructure telemetry, large-scale data engineering, and AI-driven analytics, focusing on how raw signals from hardware and networking systems can be transformed into actionable intelligence.
Starting with the foundations of telemetry, the book explores how data is generated across processors, memory systems, storage devices, and networking components. It then moves into the design of high-throughput ingestion pipelines and hyperscale data lake architectures capable of handling millions of data points in real time.
Key highlights include:
- Understanding hardware and network-level telemetry signals
- Designing distributed ingestion pipelines for high-volume data streams
- Building hyperscale data lakes for infrastructure monitoring
- Implementing streaming and batch processing systems
- Engineering clean, normalized, and ML-ready telemetry datasets
- Applying machine learning for anomaly detection, pattern discovery, and predictive analytics
The book bridges the gap between infrastructure engineering, data engineering, and artificial intelligence, enabling organizations to move from reactive monitoring to proactive and intelligent infrastructure management.
Ideal for:
- Data engineers and platform engineers
- Cloud and infrastructure architects
- Site Reliability Engineers (SREs)
- AI/ML engineers working with operational data
- Researchers and advanced students in distributed systems
This book equips readers with the knowledge and frameworks needed to build scalable, intelligent, and data-driven infrastructure systems in the era of hyperscale computing.
