Production Grade Python Automation: Scheduling, Logging, and Failure Recovery for Real Systems (en Inglés)
Reseña del libro "Production Grade Python Automation: Scheduling, Logging, and Failure Recovery for Real Systems (en Inglés)"
Stop treating production automation like a collection of scripts that only need to work when everything goes right. Prevent missed runs, silent failures, duplicate records, corrupted data, alert storms, and 3 AM emergencies before they become operational incidents. Production-Grade Python Automation is a practical engineering guide to designing, hardening, deploying, monitoring, scaling, and recovering real Python automation systems. Starting with a simple nightly data-ingestion pipeline, you will progressively transform fragile code into a production-ready system with reliable configuration, structured logging, intelligent error handling, safe scheduling, concurrency controls, crash-safe data operations, monitoring, observability, security, containerization, CI/CD, Kubernetes deployment, and automated failure recovery. Learn how to design idempotent workflows, handle retries with exponential backoff and jitter, prevent race conditions, manage time zones and daylight saving time safely, protect secrets, expose metrics with Prometheus, build dashboards with Grafana, trace complex workflows with OpenTelemetry, and deploy automation through Docker and Kubernetes. You will also explore Celery and RQ for distributed task processing, production testing strategies, dependency reproducibility, performance profiling, operational runbooks, incident troubleshooting, and scaling data pipelines from thousands to millions of records. The result is more than a script that runs successfully on your machine. You will learn how to build automation that can be observed when it fails, recover when conditions change, protect data during crashes, handle increasing workloads, and remain maintainable long after its original author is no longer available. Through practical patterns, failure scenarios, case studies, and a progressively hardened pipeline, you will see how production systems actually break—and how to design them to withstand those failures. Written for Python developers, automation engineers, DevOps engineers, platform engineers, system administrators, data engineers, and technical professionals who need to build and operate dependable automated workflows in production environments. The book is especially valuable for readers moving beyond ad hoc scripts and learning to think in terms of reliability, observability, deployment, recovery, and operational ownership. Build automation that does more than run. Build systems that can tell you when something is wrong, explain why it happened, recover intelligently, and keep working when real-world conditions are far from perfect.
