Prep for your next data engineering interview. Work through PySpark notebooks framed as real problems from Zephyr Coffee Co. (a fictional 200-store chain with messy data), review the theory docs before senior rounds, drill the quizzes the night before.
Built in the open. Contributions welcome — see below.
- pyspark/ — the PySpark module (start here)
- data_modeling/ — dimensional modeling, SCDs, star vs snowflake, grain
- ai_for_data_engineering/ — ⭐ the DE work behind LLMs: RAG/agents (using LLMs) + pre-training & SFT data (building LLMs)
- company_interviews/ — company-wise DE interview patterns (Ola, Flipkart, Swiggy, PhonePe, Jio)
- dsa/ — 🚀 authentic DE DSA syllabus & coding case studies (Google, stream windowing, reconciliation)
- ZEPHYR.md — the fictional company whose data runs through every notebook
- Roadmap — what's coming next
- Resources — thought leaders + resume examples
- Community — 💬 WhatsApp group for serious DEs
- Contributing
The PySpark module. READMEs inside guide you through it based on your level (beginner / intermediate / senior).
Hands-on notebooks (each framed as a Slack message from a Zephyr colleague asking you to solve a realistic problem):
- Syntax cheatsheet — 30-min flat reference
- Window functions — consecutive months, churn detection, top-N per group
- Joins — type mismatches, broadcast, skew detection, salting
Theory docs (10-min night-before-interview reviews):
- Shuffle & partitioning
- Memory management
- Catalyst & AQE
- Data skew playbook
- Spark UI &
.explain()debugging
Self-check quizzes (collapsible Q&A, 🟢 basics → ⚡ senior judgment):
- Window functions · Joins · Memory · Catalyst/AQE · Skew · Spark UI
The Data Engineering DSA module. Focuses strictly on patterns required to build scalable pipelines rather than general software engineering puzzles.
- Syllabus — The authentic DE DSA Syllabus mapping 6 essential patterns (Heaps, Sliding Window, Set Reconciliation, Sessionization, Two Pointers, Out-of-Core Processing) to LeetCode questions.
- Google Case Study — Deep dive into an intense 3-problem Google technical screen with production-ready Python solutions and distributed scaling strategies.
Phase 2 (next, no dates):
- Null handling & deduplication notebook (Zephyr's 2023 POS duplicate incident)
- Nested data notebook (exploding loyalty event structs)
- Structured streaming notebook
- Delta Lake notebook
- Quiz + theory coverage for each
Phase 3:
- SQL module (window functions in SQL, gaps-and-islands, SCDs, query optimization)
- Python for DE module (collections, generators, pandas↔Spark, testing)
- System design scenarios for DE interviews
- ✅ DE DSA Coding Bank & Syllabus (Google case studies, stream windowing, set reconciliation, out-of-core sharding)
The repo aims to be honest about what's built and what's not. No fake timelines.
- Sumit Mittal — Founder of BigDataBySumit
- Joe Reis — Co-author of Fundamentals of Data Engineering
- Zach Wilson — Data engineering specialist
- Shashank Mishra — Data engineer & educator
- Gowtham SB — Big data & cloud
- Manish Kumar - For questions and interview experience
- Darshil Parmar - For Crisp DE Videos
- Ansh Lamba - Best for Azure and Databricks
- Data Pathshala Preparation of Data Engineering by Manish Kumar
- Data Engineer Handbook DE Concepts
- Manish's DE resume — well-structured, shows skills/projects/experience clearly
- My resume
- Jake Overleaf - Open as template then edit it as your resume on latex
If you're serious about data engineering — interviewing, building, learning in public — I'm building a small WhatsApp community for it. Join here:
Small and serious > big and noisy.
Typo fixes, clearer explanations, new quiz questions, Zephyr scenario ideas, and blog-link additions (with a one-line justification for why it beats what's already linked) are all welcome. Open an issue or a PR.
Please don't send: random link dumps, self-promotional content, or AI-generated filler. The curation is the point — every external link in this repo was added because it's genuinely the best free resource for that topic, not because it exists.