Evening Cohorts
Python Pipelines · Evening Cohort
Scheduler-aware scripts, resilient retries, and logging that on-call teams actually read.
Overview
Evening blocks respect working hours in Daegu and remote learners across Korea. You build incremental extracts, handle schema drift, and package jobs so platform teams can operate them. We emphasize observability: structured logs, tracing IDs, and humane alerts.
What is included
- Incremental loads with watermark tables
- Retry budgets that do not stampede APIs
- Typed configs validated before deploy
- Pair debugging sessions with lab mentors
- Notebook-to-repo promotion checklist
- Dry-run harness for Airflow DAG smoke tests
- Employer office hour on incident retros
Outcomes
- Operate a two-step pipeline with monitored failures
- Document runbooks another engineer can follow at 2 a.m.
- Swap data sources with minimal downstream breakage
Lead mentor
Dante Lee
SRE background; ships Python services for a logistics analytics partner.
Logistics
Difficulty: Intermediate · Schedule: Evenings · Mentor cadence: Standard
Tools referenced: Python, Airflow, Docker
Participant questions
Python level expected?
You should be comfortable with functions, virtual environments, and reading tracebacks—we do not teach syntax week one.
Mac or Windows?
Both supported; we standardize on Docker for local Airflow when possible.
Mentor response times?
Evening cohorts get same-week async feedback; emergency pager support is not included.
Cohort notes
“Retry lesson in Python Pipelines fixed our flaky vendor pull without waking the whole team.”