> Your no-BS, practical knowledge base for becoming a Data Engineer. > Clean notes. Real-world examples. Interview-focused explanations. --- ## 👋 Welcome If you're learning Data Engineering, you're in the right place. Most people fail because they: - Jump between random tutorials - Memorize tools without understanding fundamentals - Never connect concepts to real-world systems These notes are designed to: - Build **strong fundamentals** - Teach **how things work in real projects** - Help you **think like a Data Engineer** --- ## 🧭 Learning Path > Follow this in order. Each section goes from basics → advanced → real-world usage. ### 🐍 1. [[Python for Data Engineering]] Learn Python for data processing, APIs, automation, scripting, and pipelines. ### 🧠 2. [[Structured Query Language]] Master SQL for analytics, window functions, complex joins, and interviews. ### 🏗 3. [[Data Warehouse with Snowflake]] Learn dimensional modeling, transformations, and analytics engineering workflows. ### ⚡ 4. [[Apache Spark]] Big data processing, PySpark, performance tuning, and real workloads. ### 🔁 5. [[Apache Airflow]] Orchestration, DAG design, scheduling, retries, and production pipelines. ### 📡 6. [[Apache Kafka]] Event streaming, real-time pipelines, producers, consumers, and architectures. --- ## 🧰 Code & Resources > All code, datasets, and examples used in these notes are available here: 👉 **GitHub Repository:** https://github.com/darshilparmar/DataVidhya-Data-Engineering-Course-Resources If this helps you, consider giving the repo a ⭐ --- ## 🎯 How to Use These Notes - Start from **Python → SQL → Warehouse → Spark → Airflow → Kafka** - Don’t rush tools without understanding **why they exist** - Revisit notes while building projects or teaching others --- > 🧠 _“The goal is not to learn tools. The goal is to learn how data systems actually work.”_