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5.0 (1,874)·34h 18m · 323 lectures
$3.00$12–$199up to −98%
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Production Python for Data Engineers — FAANG-Scale
Memory, AsyncIO, Pandas, PyArrow, Pydantic, CI/CD, Snowflake — plus 5 portfolio capstones and 50 FAANG interview Q&A.
5.011 reviews123 learners
- Intermediate
- 6h 51m video
- 105 lectures
- English
- Captions
- Updated 2026
Taught by Snowbrix Academy
- 6h 51m of on-demand video
- 105 lectures across 26 sections
- One-off payment — no subscription
- Lifetime access on web and mobile
- Closed captions (English)
Curriculum
25 sections105 lectures6h 51m total
- The 3 AM Story — Why Production Python Breaks5:28
- Set Up a Production Dev Environment with uv, Poetry, and Docker4:40
- The 10x Engineer Toolkit — IDE, Debugger, Profiler4:38
- Project Structure That Scales — From Script to Production Package4:47
- Python Objects Are Not What You Think — Reference Counting and GC4:32
- Mutability Traps That Kill Pipelines4:52
- Measure Before You Optimize — memory_profiler, tracemalloc, getsizeof4:44
- Memory-Efficient Patterns — __slots__, NamedTuple, Generators4:08
- When Dicts Beat DataFrames — The Lookup Problem4:59
- Sets, Frozensets, and the Membership Problem4:01
- Deque, Heapq, and the Stdlib Treasures Most Engineers Skip4:08
- Dataclass vs NamedTuple vs Pydantic — Pick the Right Container4:22
- Decorators 101 — The Function Wrapper That Powers Production4:37
- The Retry Decorator That Handles Every API Failure4:55
- Structured Exceptions — Build an Error Hierarchy That Tells You What Broke4:55
- 5 Error-Handling Patterns That Save Pipelines4:16
- CSV Reading at Scale — Chunking, Dialects, the 10GB Problem4:52
- JSON the Right Way — Streaming with ijson for GB-Scale Files4:16
- Parquet — The File Format That Powers Modern Data Engineering5:13
- End-to-End Lab — CSV to Parquet With Validation4:31
- yield — The One Keyword That Changes How You Write Python4:31
- itertools — The Module That Replaces 90 Percent of Your for Loops3:56
- Generator Pipelines — Compose ETL Steps Like LEGO4:36
- send, throw, and Generator Coroutines4:10
About this course
Tutorial code runs. Production code survives. This course teaches you to write Python the way senior engineers at Stripe, Netflix, and Snowflake actually write it — and to ship 5 portfolio repos that land you a Senior Data Engineer role. You will not just learn syntax. You'll master the Python memory model, build resilient ETL pipelines with retries and circuit breakers, pull a million API calls with AsyncIO, cut Pandas memory by 90% with dtype optimization, beat Pandas 100x with PyArrow, deploy via Docker + GitHu…
Before you start
- Comfort with Python basics — variables, functions, classes, lists, and dictionaries
- Familiarity with the command line and Git
- A laptop with Python 3.11+, Docker Desktop, and a code editor (VS Code or PyCharm)
Who it’s for
- Mid-level data engineers who want to move into senior roles — with the portfolio to back it up
- Backend and software engineers moving into data engineering who need production patterns, not tutorials
- Data analysts and engineers prepping for FAANG Senior Data Engineer interviews — moving from notebooks to deployed code
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