Learn distributed computing by building a Python web service for job management and resource-aware scheduling.
Linux Distributed Systems Programming with Python uses progressive, hands-on labs to connect each topic to a developing product, from a tested Python service to network communication, coordination, storage and operations.
Demystify Python abstractions by following execution through bytecode, memory, processes and Linux system calls. Program sockets, investigate the Linux network stack, inspect packets with Scapy and explore kernel events with eBPF. Use OpenTelemetry traces and metrics to connect what an application reports with what its underlying system is doing.
As work moves between machines, examine messaging, retries, Raft consensus, replicated storage and resource scheduling. Test what happens when workers stop, messages arrive twice or machines disagree about state. Learn how these mechanisms, together with failure isolation and recovery, contribute to high availability and fault tolerance.
Take the work into Microsoft Azure and a reader-managed on-premises cloud built with OpenStack and Kubernetes. Apply DevOps and site reliability engineering practices to automated quality checks, deployment, observability, service objectives, rollout and rollback. Required lab checks run locally without a cloud subscription; live deployment exercises use separately provisioned infrastructure.
The labs include working service capabilities and focused models of failures and algorithms. Each checkpoint gives you code to inspect and experiments to repeat, so you learn not only how distributed software is built, but why its design choices matter when you operate it.
