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5 weeks · self-paced · advanced
Advanced ETL with Modern Python
Graduates of Data Pipelines in Python can push further with columnar tools, profiling, and memory maps. We stay pragmatic—no cluster fantasy.
JPY 98,000
Informational price — no checkout here. See Returns & Refunds.
Request information
What the syllabus includes
- Polars vs pandas trade-off labs on identical tasks
- Typing patterns for dataframe helpers
- Memory profiling with fil and line_profiler
- Parquet partitioning schemes for analytics handoff
- Incremental merge strategies with keys
- Contract tests downstream analysts requested
- Documentation pass for data dictionary habits
Outcomes you can demonstrate
- Cut runtime measurably on a provided dirty dataset
- Publish profiling screenshots in your README
- Negotiate schema changes with analysts using a shared doc
Mentor of record
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Amelia Costa
Backend mentor — built ingestion for climate sensor networks.
FAQ
Comfortable with pandas idioms or equivalent experience.
Experience notes
Polars module in Advanced ETL shaved our nightly job; mentor pushed me to document partition keys.
Still slower than I hoped on week two—expected—but feedback was concrete.