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DATA ENGINEERING by Ajit Singh
English | July 20, 2025 | ISBN: N/A | ASIN: B0FJ8YVRPJ | 233 pages | EPUB | 0.49 Mb
This book is crafted to be a comprehensive, single-point resource for B.Tech and M.Tech students in India and across the globe. We recognize that the principles of building scalable, efficient, and reliable data systems are universal. Therefore, while grounding our examples in a context that is relatable and accessible, we have aligned the curriculum with the syllabi of leading international universities. The technologies discussed-from Apache Spark and Kafka to cloud platforms like AWS, Azure, and GCP-are the global industry standard.
Key Features:
1. Practical-First Approach: Every theoretical concept is immediately reinforced with easy-to-follow, practical code examples and real-world scenarios, ensuring knowledge is applicable.
2. Holistic Coverage: The ten-chapter structure provides a complete journey, covering everything from SQL/NoSQL databases, data warehousing, ETL/ELT, big data processing with Spark, stream processing with Kafka, and the crucial bridge to machine learning with MLOps.
3. Cloud-Centric: Acknowledging that the modern data world lives on the cloud, the book dedicates significant attention to building data systems using the core services of AWS, Azure, and GCP.
4. Real-Life Capstone Project: A unique, dedicated chapter guides students step-by-step through building a complete real-time sales dashboard, solidifying their skills and providing a portfolio-worthy project.
5. Simplified Language: Complex topics are explained in simple, clear, and concise language, making the book accessible to students who are new to the field.
6. Ethical and Governance Focus: A dedicated chapter on Data Governance, Quality, and Security equips future engineers with the knowledge to build responsible and trustworthy data systems.
My approach is "practical-first." Every theoretical concept is immediately followed by simple, digestible, and practical examples. We've stripped away unnecessary jargon and complexity to focus on the core logic. You will not just read about building an ETL pipeline; you will walk through the steps of building one for a real-world use case. You won't just learn the definition of data modeling; you'll design a schema for a familiar scenario. This hands-on methodology ensures that learning is not a passive activity but an active, engaging process of building and creating.
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