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Production Python for Data Engineers — FAANG-Scale

Name: Production Python for Data Engineers — FAANG-Scale
Rating: 5.0 (8 reviews)

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

Created bySnowbrix Academy

Last updated 8/2026

English

English [Auto],

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

Course content

26 sections • 105 lectures • 6h 51m total length

The 3 AM Story — Why Production Python Breaks5:28
The lecture narrates a 3am outage to show why production python breaks, highlighting memory awareness, idempotent, observable, and reproducible pipelines, with chunked reads and retries.
Set Up a Production Dev Environment with uv, Poetry, and Docker4:40
Set up a production-ready Python dev environment using uv, poetry, and docker to ensure zero version drift across laptop, CI, and production with reproducible dependencies.
The 10x Engineer Toolkit — IDE, Debugger, Profiler4:38
Explore a 10x engineer toolkit by using an ide, debugger, and profiler to diagnose bugs fast with breakpoints, remote debugging, and a flame graph.
Project Structure That Scales — From Script to Production Package4:47
Explore how a single root folder and a strict 3-layer layout scale a Python pipeline from script to production package, with pyproject as the single source of truth.

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, Generators Preview4:08
Learn memory-efficient patterns in Python data engineering using slots, NamedTuple, and generators to cut per-instance memory, optimize ETL pipelines, and compare regular classes with fixed-attribute structures.

When Dicts Beat DataFrames — The Lookup Problem4:59
Discover how dict lookups beat pandas.merge for single-column joins, slashing memory and time in large-scale data pipelines, with a practical production pattern using generator-based enrichment.
Sets, Frozensets, and the Membership Problem4:01
Compare lists and sets to demonstrate constant-time membership checks with sets for 10 million events. Explore frozensets for caching, set operations, and the memory-speed tradeoffs that optimize ETL pipelines.
Deque, Heapq, and the Stdlib Treasures Most Engineers Skip4:08
Explore deque for rolling windows, heapq for top K analytics, and bisect for online sorted insertions, showing memory-efficient patterns that appear in every data pipeline.
Dataclass vs NamedTuple vs Pydantic — Pick the Right Container4:22
Compare dict, data class, named tuple, and Pydantic for event records, weighing memory, mutability, validation, and trust boundaries in json parsing to pick the right container.

Decorators 101 — The Function Wrapper That Powers Production4:37
Learn how decorators wrap production functions to add timing, retries, and structured error handling, using wraps, tenacity, and a three-layer decorator pattern for reliable, observable code.
The Retry Decorator That Handles Every API Failure4:55
Explore exponential backoff with jitter for handling transient API failures, using a tenacity retry decorator and idempotency keys to prevent double charges.
Structured Exceptions — Build an Error Hierarchy That Tells You What Broke4:55
Build a custom exception hierarchy for data pipelines, with base pipeline errors and per-layer subclasses, contextual logging, raise from, and targeted catching for rapid debugging.
5 Error-Handling Patterns That Save Pipelines4:16

CSV Reading at Scale — Chunking, Dialects, the 10GB Problem4:52
Explore chunked csv reading with a 50,000-row chunk size to bound memory, compare pandas vs csv module streaming via dictreader, and auto-detect vendor dialects with csv.sniffer.
JSON the Right Way — Streaming with ijson for GB-Scale Files4:16
Learn to process gigabyte-scale jsons with ijson streaming, holding one object at a time for constant memory, avoiding json.load, and adopt jsonl or parquet pipelines for production.
Parquet — The File Format That Powers Modern Data Engineering5:13
Parquet uses a per-column, columnar layout with embedded schema and compression to reduce IO and boost read performance. It supports schema evolution by adding columns only and enables predicate pushdown.
End-to-End Lab — CSV to Parquet With Validation4:31
Process a csv to parquet pipeline with validation and chunking, producing validated orders.parquet with snappy compression and a dead letter queue for bad rows.

yield — The One Keyword That Changes How You Write Python4:31
Discover how generator pipelines and yield transform Python loops into memory-efficient, lazy data processing, enabling 100 million events to be handled on bounded hardware with dramatically less RAM.
itertools — The Module That Replaces 90 Percent of Your for Loops3:56
Generator Pipelines — Compose ETL Steps Like LEGO4:36
Compose etl pipelines with generator stages like lego blocks, reading csv, filtering invalid, enriching customers, transforming, and writing parquet, while memory is bounded by the largest stage.
send, throw, and Generator Coroutines4:10
Compare teal standard generators for one-way ETL pipelines and orange coroutine generators for two-way communication, illustrating stateful processors, reconfigurable bulk inserters, and generator lifecycles.

The with Statement — Why Files and Connections Need It4:31
Learn how the with statement guarantees cleanup for files and connections, preventing leaks and failures by ensuring deterministic resource release through context managers.
Custom Context Managers — Class vs @contextmanager3:17
contextlib — ExitStack, Suppress, and Tools You Did Not Know Existed2:56
Master the contextlib tools ExitStack, suppress, and null context to manage dynamic resources, swallow specific exceptions, and conditionally apply with blocks, preventing bugs and streamlining Python code.
Async Context Managers — async with for I/O-Bound Resources3:33
Compare sync plain with protocol to async with protocol and see how sync blocks the thread, while async with context managers cooperates with event loop for io-bound resources like aiohttp.

The Event Loop — Why AsyncIO Is Faster Than Threads for I/O4:12
Explore how asyncio, await, and gather enable a single event loop to handle 1000 concurrent HTTP and database calls, delivering 50x throughput over threads.
async and await — Your First Async Function and the Await Trap4:30
aiohttp — Pull 1000 API Calls Per Minute Without Hitting Rate Limits5:07
Learn to scale api calls with aiohttp by using a single client session for connection pooling, applying rate limiting with a semaphore, configuring timeouts, and a gather with return exceptions.
Backpressure — Do Not Drown the Downstream3:43
Explains backpressure in a producer and consumer pipeline by using a bounded queue to throttle producers and balance throughput, with async io and multiple consumers.

The GIL — Why Threading Does Not Speed Up CPU Work3:26
Explore why the global interpreter lock limits Python threading for CPU work and how multiprocessing and the process pool executor with submit and as completed bypass it to achieve parallelism.
multiprocessing.Pool — Distribute Work Across Cores in 5 Lines3:57
Learn to distribute work across cores with multiprocessing.Pool using pool.map, tune chunk sizes, and avoid pickling pitfalls by using top-level functions; explore process pool executor for modern code.
ProcessPoolExecutor — The Modern API for Parallel ETL3:34
Learn to use ProcessPoolExecutor for parallel ETL with streaming results, as_completed, and futures mapped to inputs. Log per-file progress and manage memory and startup costs with lean imports.
Shared State — When You Actually Need It and Why You Usually Do Not3:58
Explore when to share state in data engineering pipelines, contrasting share nothing with shared memory and manager dicts for safe synchronization. Learn fan-out and reduce patterns, avoiding hot path mutations.

dtype Optimization — The 90 Percent Memory Reduction Trick4:45
Master dtype optimization in pandas by downcasting to int32 and float32 and using category for strings to reduce a 10 million row dataframe from 2 gigabytes to about 200 megabytes.
Chunked Reading — Process 50GB CSVs Without OOM3:34
Vectorization — When Apply Is the Wrong Answer4:55
MultiIndex, GroupBy, Pivot — The Patterns Senior Pandas Devs Use4:56

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

Who This Course Is For

Mid-level data engineers moving into senior roles
Backend / software engineers transitioning into data engineering
Data analysts levelling up from notebooks to production code
ML engineers shipping feature pipelines, not just models
Engineers preparing for FAANG Senior Data Engineer interviews
Anyone tired of writing tutorial code and ready to ship production pipelines

Who this course is 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

Production Python for Data Engineers — FAANG-Scale

What you'll learn

Explore related topics

Course content

Why Most Python Code Fails in Production4 lectures • 20min

The Memory Model That Separates Juniors From Seniors4 lectures • 18min

Pick the Right Data Structure — 10x Performance Without Optimizing4 lectures • 18min

Decorators, Retries, and the Art of Failing Gracefully4 lectures • 19min

From CSV to Parquet — File I/O for Real Production Data4 lectures • 19min

Generators — How Netflix Processes 100GB Files on a Laptop4 lectures • 17min

Context Managers — Connections That Never Leak, Even at 3 AM4 lectures • 14min

AsyncIO — Pull a Million API Calls Without Breaking Anything4 lectures • 18min

Multiprocessing — Use All 32 Cores Like a Senior Engineer4 lectures • 15min

Pandas at Scale — Cut Memory by 90 Percent Without Changing Logic4 lectures • 18min

Requirements

Description

Who this course is for: