June 6, 2026

Big Data Engineering (with Claude Code)

By Savkee Tutorials 0 Comments

Build a production-grade data platform in 12 Weeks, with Claude Code as your pair engineer
What you’ll learn
⚡ Build a production-grade 7-layer data platform end-to-end with storage, compute, transform, stream, orchestrate, validate, and serve, using open-source tools.
⚡ Process millions of rows with PySpark, write distributed batch pipelines, read Spark execution plans, and tune slow joins with broadcast hints and AQE.
⚡ Build a Lakehouse with Apache Iceberg, ACID transactions, time-travel queries, snapshot management, and painless schema evolution on object storage.
⚡ Model analytical data with dbt, layered staging→marts projects, automated tests, generated docs, and lineage DAGs that analysts can trust.
⚡ Stream events with Kafka and Flink, build a real-time fraud detection consumer and stateful tumbling and sliding window aggregations using PyFlink.
⚡ Orchestrate pipelines with Airflow, author DAGs, manage dependencies, pass data with XCom, and add retry/alert logic to a nightly end-to-end ETL.
⚡ Enforce data quality with Great Expectations and ship self-service dashboards in Apache Superset on top of a curated DuckDB analytical mart.
⚡ Build a RAG pipeline on your warehouse, embed policy docs with SentenceTransformers, index in ChromaDB, and ground OpenAI API answers semantically.
⚡ Master Claude Code as a pair engineer, design prompting strategies, learn file-context patterns, anchored upon the trust-but-verify discipline.
⚡ Walk away with an end-to-end portfolio project, a runnable GitHub repo of the full data platform to demo in interviews and link from your résumé.
Requirements
❗ Basic Python. Write functions, use loops, work with lists and dicts. No prior PySpark, Flink, Kafka, dbt, or other course-tool experience needed.
❗ Basic SQL. SELECT, JOIN, WHERE, GROUP BY, ORDER BY. We build on this with window functions, CTEs, and dbt-flavored analytical SQL during the course.
❗ Command-line basics. Navigate directories, run commands, edit files in macOS, Linux, or Windows WSL2. If you’ve used `cd` and `ls`, you’re ready.
❗ A laptop with at least 8 GB RAM (16 GB recommended) and Docker Desktop installed. All 12 labs run locally, no cloud account, no cloud bill, ever.
❗ Curiosity and persistence. Labs will break, errors will appear, and you’ll learn the stack by debugging real failures.
❗ Claude Code installed with an Anthropic API key or Claude subscription. Setup is covered in Foundation Module F4, no prior AI tooling experience required. (Optional, relevant for pair engineering only)
❗ A code editor you’re comfortable with. VS Code or Cursor recommended for first-class Claude Code integration, but any editor works fine. (Optional, relevant for pair engineering only)
❗ Willingness to pair with an AI assistant. You don’t need to be a power user; you just need to be open to a new way of building software. (Optional, relevant for pair engineering only)
Description
Data engineering is the fastest-growing role in the technology industry, and this course is your complete, practical guide to mastering it.
Most data engineering courses teach tools in isolation. You learn Spark in one course, Kafka in another, and dbt somewhere else. By the end, you have a collection of disconnected skills but no idea how to wire them together into a real platform. This course is different.
Over 12 structured weeks, you will build a complete, production-grade data platform forDataShop, a fictional global e-commerce company processing 2 million orders per day. Every week, you add a new layer to the same platform: first the storage foundation, then the batch processing engine, then the Lakehouse, then real-time streaming, then orchestration and data quality, and finally analytics dashboards and an AI-powered assistant. By Week 12, you have not just learned the tools, you have built something that works end to end.
The course covers the full modern data stack:Apache Spark for distributed batch processing,Apache Kafka andApache Flink for real-time event streaming,Apache Iceberg for the Data Lakehouse,dbt for version-controlled SQL transformations,Apache Airflow for pipeline orchestration,Great Expectations for data quality,Apache Superset for dashboards, andChromaDB for Retrieval-Augmented Generation (RAG) AI pipelines.
Every chapter is paired with a standalonePractice Lab, a realistic, hands-on exercise grounded in the DataShop scenario. You will not be copying tutorial code; you will be solving engineering problems. All labs run locally using Docker, so there are no cloud costs.
What makes this course unlike anything else:Claude Code, while optional, may be used as your pair engineer the entire way. You’ll learn the prompting patterns, file-context strategies, and trust-but-verify workflows that turn a six-hour debugging session into a forty-minute one. You’ll read Spark execution plans together, refactor brittle DAGs together, and ship features faster than you thought possible, without skipping the fundamentals that make a senior engineer senior.
Whether you are a software engineer pivoting into data, a data analyst ready to build your own pipelines, or an aspiring data engineer who wants a rigorous, concept-first education, this course will give you the architecture, the code, and the confidence to build the modern data platform.
Who this course is for
⭐ Software engineers and backend developers pivoting into data engineering who want to learn distributed systems, streaming, and the analytical-data world.
⭐ Data analysts and data scientists who want to build their own pipelines and stop waiting on engineering for every new dataset or dashboard refresh.
⭐ Aspiring data engineers with Python and SQL basics who want one rigorous, project-driven path into the field, not 47 disconnected YouTube tutorials.
⭐ Tech leads, staff engineers, and architects evaluating modern-stack tradeoffs: batch vs. streaming, Lakehouse vs. warehouse, ELT vs. ETL, where AI fits.
⭐ ML engineers and AI builders who want a proper warehouse, real data quality, and a working RAG pattern they can adapt to their own LLM applications.
⭐ Bootcamp grads and self-taught engineers ready to bridge the gap between "I finished a course" and "I can build something a real company would run."
⭐ Career changers from analytics, finance, or operations with the Python and SQL basics who want a structured, end-to-end on-ramp into a data role.
⭐ Senior engineers exploring AI-paired workflows, the prompting patterns, file-context strategy, and trust-but-verify habits transfer to your day job.
Homepage

https://anonymz.com/?
https://www.udemy.com/course/big-data-engineering

https://rapidgator.net/file/e3e13864d2ca393d1ebfddef4f2c1529/Big_Data_Engineering_(with_Claude_Code).part5.rar.html
https://rapidgator.net/file/77612e1124a9c19699172766e0f0b31c/Big_Data_Engineering_(with_Claude_Code).part4.rar.html
https://rapidgator.net/file/f6ac6944bc3041cda770c5813fe4bc40/Big_Data_Engineering_(with_Claude_Code).part3.rar.html
https://rapidgator.net/file/ea0f7a16cda47e93672325082186d8e5/Big_Data_Engineering_(with_Claude_Code).part2.rar.html
https://rapidgator.net/file/f93fa7f2ae18e964988e252a0dedec8f/Big_Data_Engineering_(with_Claude_Code).part1.rar.html

Tags:Big, Claude, Code, Data, Engineering

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