Production Python for Data Engineers — FAANG-Scale

打造FAANG级高并发ETL管道

深入掌握Python内存模型与asyncio，学习构建超大吞吐量生产级数据管道。课程含工业实战、50道FAANG面试解析，助你冲击高阶数据工程岗位。

本课程以生产环境高并发场景为核心，深入剖析 Python 内存模型与 asyncio 机制，教授如何构建超大吞吐量 ETL 数据管道。课程结合工业级项目实战与面试题解析，旨在提升高级数据工程能力并助力冲击高阶职位。

Published 6/2026
Created by Snowbrix Academy
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 105 Lectures ( 7h 1m ) | Size: 3 GB

Memory, AsyncIO, Pandas, PyArrow, Pydantic, CI/CD, Snowflake — plus 5 portfolio capstones and 50 FAANG interview Q&A.

What you’ll learn
⚡ Diagnose memory leaks and OOM crashes using tracemalloc, memory_profiler, and the Python object model
⚡ Pick the right data structure (dict vs DataFrame, sets, deque, heapq, dataclass, NamedTuple, Pydantic) for 10x performance with zero optimisation
⚡ Build retry, circuit breaker, and idempotency patterns that survive vendor API outages and 3 AM pager calls
⚡ Process 100GB CSV/JSON/Parquet files on a laptop using chunked I/O, generators, and ijson
⚡ Pull a million API calls per minute with AsyncIO — the event loop, await trap, aiohttp, backpressure
⚡ Cut Pandas memory by 90% with dtype optimisation, then beat Pandas 100x with PyArrow zero-copy reads
⚡ Ship every change through GitHub Actions CI/CD with matrix builds, secrets, AWS Lambda/ECS deploys, and blue-green rollouts
⚡ Build 5 portfolio capstones (Stripe to Snowflake, Kafka, MySQL CDC, ML feature store) plus 50 solved FAANG interview questions

Requirements
❗ 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)

Description

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 + GitHub Actions to AWS, and ship 5 capstone projects that become the GitHub repos on your resume.

What makes this course different
✨Story-driven lessons. Every module opens with a real 3 AM incident — not a feature list.

✨Code-first. Over 40% of slides are runnable, copy-paste-ready Python.

✨Production mindset. Idempotency, observability, graceful degradation — the senior-engineer mental models that separate juniors from staff engineers.

✨Five real capstones. Not toy projects — GitHub-shippable repos with benchmarks, resume bullets, and interview Q&A baked in.

✨FAANG interview prep. 50 fully-solved questions covering memory, concurrency, system design, and pipeline architecture.

✨Zero hallucination. Real syntax, real libraries (Pydantic v2, PyArrow, tenacity, structlog, OpenTelemetry, aiohttp, aiokafka), real production patterns.

The Python you will master: the memory model (reference counting, GC, mutability traps, __slots__, generators), the right data structure for the job (dict vs DataFrame, sets, deque, heapq, dataclass vs NamedTuple vs Pydantic), decorators and retry patterns (functools, tenacity, structured exceptions), file I/O at GB scale (chunked CSV, ijson for JSON, Parquet), generators and itertools for memory-bounded pipelines, context managers (with, contextlib, async with), AsyncIO end-to-end (event loop, await trap, aiohttp, backpressure), multiprocessing (GIL, Pool, ProcessPoolExecutor, shared state), Pandas at scale (dtype optimization, chunked reading, vectorization, MultiIndex), PyArrow (zero-copy, columnar, 100x faster Parquet), resilient API ingestion (sessions, tenacity, circuit breakers, idempotency), SQLAlchemy + psycopg2 + Snowflake connector, the PySpark bridge for distributed thinking, Pydantic v2 for data contracts, dependency management (pip, poetry, uv, pyproject.toml, Docker), production ETL framework architecture, chunked processing for petabyte scale, idempotency patterns, structured logging with structlog + Prometheus + OpenTelemetry, pytest and mocking with moto/responses, GitHub Actions CI/CD with AWS Lambda/ECS deployment, blue-green deploys, and the senior-engineer mental models that make all of it stick.

The 5 capstones (your future GitHub portfolio)
✨Capstone 1: E-Commerce ETL — Stripe API → async ingestion → Pydantic validation → Snowflake with schema evolution.

✨Capstone 2: Log Analytics Platform — 1GB logs → streaming parse with generators → anomaly detection → dashboard.

✨Capstone 3: Real-Time Kafka Pipeline — aiokafka consumer → async processing → Redshift writer.

✨Capstone 4: MySQL CDC Migration — watermark-based CDC → Parquet lake → schema tracking.

✨Capstone 5: Snowflake ML Feature Store — feature engineering → drift detection → Slack/email alerting.

Each capstone ships with benchmarks, resume bullets, and a full interview Q&A walkthrough.

Plus 50 fully-solved FAANG interview questions: 25 technical (memory, concurrency, pipelines) + 25 system design and behavioural. Every answer reasoned through end-to-end, the way you’d answer them in a real on-site.

Who this is for
✨ Mid-level data engineers who want to break into senior — and have the resume to back it up

✨ Software engineers transitioning into data engineering who need Python production patterns, not tutorials

✨ Backend engineers who keep getting handed “the data pipeline” and want to actually build it well

✨ Data analysts levelling up to engineering — moving from notebooks to deployed code

✨ Anyone preparing for FAANG Data Engineer or Senior Data Engineer interviews — memory, concurrency, system design, and behavioural all covered

✨ ML engineers who want to ship production feature pipelines (not just notebooks)

By the end of this course, you will be able to architect, build, test, deploy, and operate production Python data pipelines — and walk into Senior Data Engineer interviews at FAANG-tier companies with a portfolio that proves it.
Enrol now. Production Python is the difference between a job offer and a rejection. Make it your edge.

What You’ll Learn (15 bullets — Udemy max)
✨ Diagnose memory leaks and OOM crashes using tracemalloc, memory_profiler, and the Python object model

✨ Pick the right data structure (dict vs DataFrame, sets, deque, heapq, dataclass, NamedTuple, Pydantic) for 10x performance with zero optimisation

✨ Build retry, circuit breaker, and idempotency patterns that survive vendor API outages and 3 AM pager calls

✨ Process 100GB CSV/JSON/Parquet files on a laptop using chunked I/O, generators, and ijson

✨ Pull a million API calls per minute with AsyncIO — the event loop, await trap, aiohttp, backpressure

✨ Use all 32 CPU cores with multiprocessing.Pool, ProcessPoolExecutor, and shared-state patterns

✨ Cut Pandas memory by 90% with dtype optimisation, then beat Pandas 100x with PyArrow zero-copy reads

✨ Ship resilient API ingestion with requests sessions, tenacity, rate limiting, circuit breakers, and idempotent upserts

✨ Use SQLAlchemy Core (not ORM) for bulk loads + the Snowflake connector done right with COPY tricks

✨ Bridge from Pandas to PySpark with the distributed mental model — DataFrame API, partitioning, broadcast joins

✨ Make every pipeline self-validating with Pydantic v2 schemas, settings, and untrusted-JSON parsing

✨ Build production ETL frameworks with pluggable extract/transform/load layers, bounded queues, and graceful degradation

✨ Make every pipeline restart-safe with idempotency keys, watermarks, checkpoints, and dedup strategies

✨ Add structured logging (structlog), metrics (Prometheus), and tracing (OpenTelemetry) so 3 AM debugging takes 5 minutes, not 5 hours

✨ Ship every change through GitHub Actions CI/CD with matrix builds, secrets, AWS Lambda/ECS deploys, and blue-green rollouts

Requirements
✨ Comfort with Python basics — variables, functions, classes, lists, dictionaries

✨ Familiarity with the command line and Git

✨ A laptop with Python 3.11+, Docker Desktop, and a code editor (VS Code or PyCharm)

✨ Optional but useful: a free Snowflake trial (for Capstones 1 and 5) and an AWS free-tier account (for Module 22 + Capstone 3)

✨ No prior data engineering experience required — we start from production Python fundamentals and build to FAANG-grade pipelines