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Run your own S3-compatible storage without fooling yourself about durability: bucket policies and least privilege, presigned URLs, direct browser uploads, why a single node is not a backup, mirroring, lifecycle rules, and when a managed provider wins.
Run several Django applications on a single machine without them taking each other down: label-driven routing, automatic certificates, network and resource isolation, independent deploys, per-app backups, and knowing when one server is no longer enough.
Sending mail is easy; arriving is not. Authentication records explained properly, the alignment rule most guides omit, why fresh servers cannot send, asynchronous delivery, bounce handling, and the headers that keep you out of the spam folder.
DNS treated as the distributed system it is: which records matter, what TTL really controls, why propagation is the wrong word, negative caching, CAA to restrict certificate issuance, and how to move a domain without a gap in service.
Everything a Django deployment needs to survive losing a server: consistent database dumps, media and secrets, encrypted offsite copies with restic, retention that survives ransomware, point-in-time recovery, and the restore drill nobody runs.
A complete playbook for moving a running Django site to a new server: inventory, runtime parity, database and media transfer, certificates before DNS, the delta-sync window, the IPv6 trap that breaks TLS issuance, and how to keep a working fallback.
Find your breaking point before your users do: designing realistic load scenarios, driving them with Locust or k6, reading the latency percentiles that matter, locating the true bottleneck, and turning results into capacity plans and autoscaling signals.
Move Django file storage off local disk: django-storages with S3-compatible object storage, private files via signed URLs, direct browser uploads with presigned POST, streaming large files, async image pipelines, and CDN + security pitfalls.
Stop guessing why your app is slow. Profile a live Django process without restarting it using py-spy, trace per-request queries and timings with django-silk, read flame graphs, and turn findings into concrete fixes.
Ship to production continuously without big-bang risk. Build a feature-flag layer in Django, roll features out to a percentage of users, run A/B experiments, and add instant kill switches for when something goes wrong.
A pragmatic Kubernetes setup for a real Django app: Deployment + Service + Ingress, ConfigMaps and Secrets done right, liveness/readiness probes that work, HPA on the right metric, and PgBouncer in front of PostgreSQL.
Three pillars — logs, metrics, traces — wired into a real Django app. Sentry for errors, structlog for context, django-prometheus for golden signals, OpenTelemetry for distributed traces. The minimum to debug prod without ssh-ing in.
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