Python Foundations
Python from notebook to pipeline: type hints, dataclasses, error handling, logging, and the OOP + functional patterns that turn one-off scripts into reusable code.
Phases
4
PlatformIncluded in Free
Python for Data Engineers
OOP, functional programming, pandas, and production-ready code patterns.
Every interview that asks "can you write Python?" actually means "can you write production code?" The difference between a notebook and a pipeline is the difference between junior and your first real role.
What you'll do
What you'll be able to do.
- Write production-ready Python with proper error handling
- Process data at scale with pandas and polars
- Build ETL pipelines with testing and quality checks
- Deploy Python data services to cloud environments
Phase roadmap.
Phase 1PRO REQUIRED
Build Production Python
Core Python, OOP, data structures
1.1
✓Python Foundations
Python from notebook to pipeline: type hints, dataclasses, error handling, logging, and the OOP + functional patterns that turn one-off scripts into reusable code.
1.2
✓Build Your First Data Pipeline
Your first end-to-end pipeline: extract from an API → transform with Polars → load to Postgres + S3. The shape every production pipeline rhymes with.
Phase 2PRO REQUIRED
Build Data Pipelines
Data manipulation, quality, reliability
2.1
✓Data Manipulation
Polars vs pandas decision matrix, lazy frames + streaming, joins/groupby/pivot at scale, and when single-machine processing beats Spark.
2.2
✓Data Quality & Reliability
Pydantic validation, pytest fixtures + parametrize, property-based testing with Hypothesis, retry/backoff patterns, and the dead-letter queue every pipeline needs.
Phase 3PRO REQUIRED
Scale & Deploy
ETL, performance, distributed, cloud deployment
3.1
✓ETL Pipelines
SQLAlchemy 2.0 connections, upsert + bulk-insert patterns, transaction management, idempotent writes, and the pipeline structure that survives mid-run failures.
3.2
✓Performance & PySpark
PySpark DataFrame fundamentals, Pandas UDFs vs vectorized UDFs, async I/O with asyncio for API ingestion, multiprocessing for CPU-bound work, and profiling with cProfile.
3.3
✓Cloud Deployment
Cloud storage patterns (S3 + GCS + Azure Blob), Docker multi-stage builds, Lambda / Cloud Functions deployment, secrets management, and the IAM least-privilege setup that doesn't break in production.
3.4
✓Production Capstone
End-to-end pipeline architecture: design → ingest → transform → load → monitor → deploy. Plus the production-grade decisions (retries, idempotency, observability) that separate juniors from mid-level.
Phase 4PRO REQUIRED
AI Data Engineering
LLMs, embeddings, RAG pipelines, async batching
4.1
✓AI Data Engineering
LLM API fundamentals (OpenAI / Anthropic SDKs), embeddings + vector search basics, prompt + response evals, async batching for LLM throughput, and where Python data engineering meets AI infra.
What you'll ship
What you'll build.
- End-to-end ETL pipeline with Polars + SQLAlchemy + Postgres and retry-safe API ingestion
- Pytest + Pydantic + Hypothesis test suite that catches data issues before deploy
- Dockerized Python service deployed to AWS Lambda with IAM least-privilege
- LLM-powered enrichment job with async batching + cost tracking — your first AI pipeline
Definition
What is Python?
Python for data engineering focuses on writing production-grade code for data pipelines, ETL processes, and data services. Unlike data science Python, data engineering Python emphasizes OOP, error handling, testing, and deployment — the patterns used at companies like Spotify, Airbnb, and Uber to process terabytes daily.
Production context
Why this matters in production.
Python is the primary language for data pipeline orchestration, API integrations, and custom transformation logic. At Netflix, Python orchestrates thousands of data jobs daily. Production Python requires proper error handling, logging, and testing — not just notebook-style scripting.
Use cases
Common use cases.
- Building ETL pipelines that extract, transform, and load data across systems
- Processing large datasets with Polars and PySpark for performance
- Writing API integration scripts with proper retry logic and error handling
- Creating data quality validation frameworks with pytest
- Deploying containerized data services to cloud platforms
- Orchestrating workflows with Airflow DAGs written in Python
Compare
Python vs alternatives.
PythonvsJava
Python offers faster development and a richer data ecosystem. Java provides better performance for low-latency systems. Most data teams choose Python for pipeline logic and reserve Java/Scala for framework-level code.
PythonvsScala
Python dominates data engineering due to PySpark and broader library support. Scala is preferred for Spark-native development and performance-critical jobs. PySpark bridges both worlds.
PythonvsSQL
Python handles orchestration, API calls, and complex logic. SQL handles warehouse transformations. Production data engineers use both — Python for pipeline code, SQL for data transformations.
Before you start
Before you start.
Tech stack
- Python
- Polars
- PySpark
- pytest
- Docker
Prerequisites
- Basic programming experience
- Familiarity with any scripting language
Why this matters
Why this skill matters.
Production Python is the line between writing notebooks and writing pipelines. Once you can structure a project, handle errors, test the edges, and deploy to cloud — you've cleared the bar for junior data engineering roles AND set up the foundations for every senior trajectory (batch, streaming, ML, platform).
FAQ
Common questions about Python.
Python for Data EngineersStart Phase 1