Hello Modern Data Pipelines (Raj Kishore Singh)（Z-Library）

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Hello Modern Data Pipelines A practical guide to designing and operating modern data pipelines Raj Kishore Singh www.bpbonline.com

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First Edition 2026 Copyright © BPB Publications, India ISBN: 978-93-65894-837 All Rights Reserved. No part of this publication may be reproduced, distributed or transmitted in any form or by any means or stored in a database or retrieval system, without the prior written permission of the publisher with the exception to the program listings which may be entered, stored and executed in a computer system, but they can not be reproduced by the means of publication, photocopy, recording, or by any electronic and mechanical means. LIMITS OF LIABILITY AND DISCLAIMER OF WARRANTY The information contained in this book is true and correct to the best of author’s and publisher’s knowledge. The author has made every effort to ensure the accuracy of these publications, but the publisher cannot be held responsible for any loss or damage arising from any information in this book. All trademarks referred to in the book are acknowledged as properties of their respective owners but BPB Publications cannot guarantee the accuracy of this information. www.bpbonline.com

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Dedicated to My beloved parents Shri Umesh Prasad Singh Smt. Malti Devi, my wife Sony Singh, my son Druvish, and my daughter Shrivika

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About the Author Raj Kishore Singh is a principal engineer with over 14 years of experience in software and data engineering. He specializes in designing and building large-scale, cloud-native data platforms, with a strong focus on scalability, reliability, and real-time processing. He holds a master of technology degree in intelligent systems from IIIT Prayagraj, where he developed a strong foundation in data-driven systems, distributed computing, and intelligent decision-making. This academic background complements his industry experience and informs his approach to designing robust and scalable data architectures. Over the course of his career, Raj has been involved in building and operating data systems across multiple domains, working with modern data engineering tools and frameworks such as distributed processing engines, streaming platforms, orchestration systems, and analytical data stores. His work emphasizes practical architecture and production-ready solutions that can evolve with changing business and technical requirements. He has a strong interest in modern data pipeline architecture, real-time data processing, data governance, and operational reliability, and is passionate about simplifying complex data engineering concepts for practitioners at different stages of their careers. This book reflects his combined industry and academic experience and his commitment to helping engineers build scalable, reliable, and future-ready data pipelines.

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About the Reviewers ❖ Srinivas Rangu is a seasoned technology leader with 18+ years of experience in the data and technology landscape, including over 5 years in strategic leadership roles. He specializes in data engineering, analytics, and data governance, with a proven track record of leading complex data migration and modernization initiatives that drive efficiency and streamline operations. Srinivas architected robust, scalable data solutions that empower organizations to extract actionable insights and adapt to fast-changing business demands. His leadership on high-impact programs has resulted in measurable gains in operational efficiency, risk reduction, and long- term business value. Passionate about people and performance, he thrives on building and mentoring high-performing teams, helping individuals grow while fostering a culture of excellence. Srinivas is currently working as director, data technologies at Nationwide Mutual Insurance, located in Columbus, OH, USA. ❖ Viba Renganathan is an internationally recognized technology leader and global transformation expert with more than 13 years of experience leading large-scale, cross-border enterprise modernization across North America, Europe, and Asia. She serves as a project manager at Munich Re America Services, part of one of the world’s largest reinsurance and financial services groups, where she leads highly regulated cloud, cybersecurity, and data-center transformation programs impacting hundreds of enterprise applications worldwide. A published thought leader in CIO.com and an internationally invited judge for the Stevie Awards, Entrepreneurship World Cup, and Stratus Awards for Cloud Computing, Viba is also a Global Relations Co-Chair of the Society of Women Engineers (SWE) Pittsburgh Chapter and a representative in

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the Women in Data leadership program, where she mentors and supports the next generation of technology leaders.

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Acknowledgement I would like to express my heartfelt gratitude to my elder brother, Ranjeet Kumar, whose guidance, wisdom, and unwavering encouragement have been a guiding light throughout my education and career journey. I am also thankful to my younger brothers, Jay and Pravin, for their constant support and encouragement. I am deeply grateful to my entire family for their love, patience, and support, which made this work possible. I would also like to sincerely thank the team at BPB Publications for their support, understanding, and flexibility in providing me with the time needed to complete this book. Their encouragement and cooperation played an important role in bringing this work to completion.

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Preface In today’s digital economy, data is generated continuously by applications, platforms, devices, and users. Organizations depend on this data to drive decisions, automate processes, and deliver real-time experiences. However, the true value of data lies not in its volume, but in the ability to reliably ingest, process, transform, and deliver it through well-designed data pipelines. As data systems grow in scale and complexity, building robust and maintainable pipelines has become a central responsibility of modern data engineering. This book is written to provide a clear and practical understanding of how modern data pipelines are designed, built, and operated in real-world environments. Rather than focusing on individual tools in isolation, the book takes a holistic view of data engineering. It explains how different components work together, why architectural decisions matter, and how pipelines evolve as business and technical requirements change. The goal of this book is to help readers develop both a strong conceptual foundation and practical skills. Emphasis is placed on scalability, reliability, observability, and governance, which are essential qualities of production- grade pipelines. Through real-world examples and hands-on exercises, readers are encouraged to reason about trade-offs involving latency, consistency, cost, and long-term maintainability. The book follows a progressive structure. Early chapters establish core concepts and design principles. Subsequent chapters explore ingestion, processing, storage, governance, and real-time systems. The later chapters focus on orchestration, end-to-end implementation, troubleshooting, and emerging trends, preparing readers to operate pipelines confidently in real production environments. This book is divided into ten chapters, each building on the previous one to form a complete view of modern data pipeline systems. The details are

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outlined in the following: Chapter 1: Introduction and Overview- This chapter explains why data pipelines are critical in today’s data-driven world. It explores the data explosion, the need for real-time insights, and the evolution from traditional batch ETL to modern cloud-native architectures. The chapter also introduces the defining characteristics of modern pipelines, such as scalability, modularity, observability, and governance. Chapter 2: Data Engineering Essentials- This chapter introduces the core principles of data engineering, including ETL and ELT patterns and the fundamental components of a data pipeline. Readers set up a local development environment and run their first PySpark-based example, while also learning best practices for building reliable and modular pipelines. Chapter 3: Designing Scalable Pipeline Architecture- This chapter focuses on architectural design and scalability. It examines common patterns such as Lambda, Kappa, and hybrid architectures, and discusses practical techniques including partitioning, parallelism, error handling, schema evolution, and observability, supported by real-world examples. Chapter 4: Advanced Data Ingestion and Integration- This chapter covers ingesting data from diverse sources such as databases, APIs, files, and event streams. The chapter explains batch and streaming ingestion models, reliability mechanisms, and modern ingestion frameworks, with hands-on examples using Kafka and Spark. Chapter 5: Data Processing and Transformation- This chapter explains how raw data is transformed into analytics-ready datasets. Readers learn core transformation techniques, compare batch and real-time processing approaches, and implement efficient and maintainable transformation workflows. Chapter 6: Strategic Data Storage and Management- This chapter helps readers choose appropriate storage systems by comparing data lakes, data warehouses, and traditional databases. It also introduces best practices for data organization, access control, retention, and regulatory compliance. Chapter 7: Ensuring Data Quality, Governance, and Security- This chapter emphasizes building trust in data pipelines. The chapter covers

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validation techniques, governance principles, ownership models, and essential security practices such as encryption, access control, and auditing. Chapter 8: Real-time Processing and Orchestration- This chapter focuses on designing low-latency systems using streaming architectures. Readers explore reliability concepts, delivery semantics, orchestration tools, and monitoring practices, supported by a hands-on real-time clickstream pipeline. Chapter 9: Case Studies and End-to-end Project- This chapter brings together all previous concepts through a complete, working pipeline that integrates streaming, batch processing, orchestration, and analytics, demonstrating how pipelines are operationalized in practice. Chapter 10: Troubleshooting, Optimization, and Future Trends- This chapter focuses on operating pipelines at scale. It introduces structured troubleshooting techniques, performance optimization as a continuous practice, and emerging trends that will shape the future of data engineering. By the end of this book, readers should feel confident designing, building, and operating scalable, reliable, and future-ready data pipelines.

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Code Bundle and Coloured Images Please follow the link to download the Code Bundle and the Coloured Images of the book: https://rebrand.ly/5f321a The code bundle for the book is also hosted on GitHub at https://github.com/bpbpublications/Hello-Modern-Data-Pipelines. In case there’s an update to the code, it will be updated on the existing GitHub repository. We have code bundles from our rich catalogue of books and videos available at https://github.com/bpbpublications. Check them out! Errata We take immense pride in our work at BPB Publications and follow best practices to ensure the accuracy of our content to provide with an indulging reading experience to our subscribers. Our readers are our mirrors, and we use their inputs to reflect and improve upon human errors, if any, that may have occurred during the publishing processes involved. To let us maintain the quality and help us reach out to any readers who might be having difficulties due to any unforeseen errors, please write to us at : errata@bpbonline.com Your support, suggestions and feedbacks are highly appreciated by the BPB Publications’ Family. At www.bpbonline.com, you can also read a collection of free technical articles, sign up for a range of free newsletters, and receive exclusive discounts and offers on BPB books and eBooks. You can check our social media handles below:

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Table of Contents 1. Introduction and Overview Introduction Structure Objectives Data pipelines in the digital era Data explosion and real-time demands Role in business and innovation Evolution of data pipelines Stage 1: Traditional ETL Stage 2: Real-time and streaming pipelines Stage 3: Cloud-native, decoupled pipelines Defining modern data pipelines Scalable by design Decoupled and modular Schema-aware and schema-evolving Governed and secure Observable and testable Automated and orchestrated Common pipeline use cases across industries Finance and fintech E-commerce and retail Telecommunications Healthcare and life sciences CRM, marketing, and customer analytics

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Conclusion 2. Data Engineering Essentials Introduction Structure Objectives Core data engineering concepts Core patterns in data movement Data integration fundamentals Importance of data integration Key challenges in data integration Common data integration patterns Best practices for data integration Components of data pipelines Data ingestion Types of data sources Ingestion modes Ingestion strategies Data processing Activities involved in data processing Processing approaches Key considerations Data storage Data presentation Common presentation interfaces Key considerations Setting up your technical environment Tools and technologies Benefits of using a virtual environment Setting up the environment Demonstration of a Word Count application

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Example project structure Example code word_count.py Running the demo application Best practices and common pitfalls Practical tips for data pipeline development Common pitfalls and avoiding them Conclusion Check your understanding 3. Designing Scalable Pipeline Architecture Introduction Structure Objectives Foundation of good design Strategic pipeline design Importance of design Balancing practical trade-offs Engineering with flexibility in mind From patterns to principles System reliability and resilience Core data pipeline design principles Modularity and separation of concerns Idempotency and reprocessing safety Observability and transparency Scalability and parallelism Schema management and evolution Configuration over code Graceful degradation and fallbacks Architectural patterns and design models Lambda architecture Kappa architecture

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Hybrid architectures Hybrid architecture scenarios Retail pipeline case study Comparing Lambda, Kappa, and hybrid architectures Practical design techniques for scalability Practical modularity techniques Data partitioning and parallelism Fault tolerance and error handling Observability and monitoring Schema evolution and compatibility Cost-aware design considerations Real-world case study Architecture design and evolution Phase 1: Stream-enabling customer signals Phase 2: Modularizing and unifying the platform Lessons learned Conclusion Check your understanding 4. Advanced Data Ingestion and Integration Introduction Structure Objectives Data extraction fundamentals Data source diversity Extraction techniques Data loading strategies Batch vs. real-time vs. micro-batch Selecting the right strategy Loading mechanisms and implementation patterns Loading into data warehouses

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Loading into data lakes Choosing the right mechanism Managing multi-source ingestion Schema alignment and harmonization Event time synchronization De-duplication and conflict resolution Data quality monitoring across sources Common patterns and practices Ingestion tools and frameworks Kafka Connect Debezium Google Cloud Dataflow AWS Glue Custom Spark framework Ensuring reliable data ingestion Common ingestion failure scenarios Strategies for reliable ingestion Observability and alerting Practical coding examples Setting up Kafka locally and publishing data Prerequisite steps for installing Docker Setting up Kafka Install the required Python libraries Create the Python JSON producer Run the producer Verify the published messages Ingesting data from Kafka to the file system Required Python libraries Sample YAML configuration Data ingestion code Running the job

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Code reference Conclusion Check your understanding 5. Data Processing and Transformation Introduction Structure Objectives Unpacking ETL versus ELT strategies ETL pipeline for telecom streaming events Pipeline flow ELT pipeline for e-commerce streaming events Pipeline flow Key differences between ETL and ELT Techniques for data transformation Cleansing and normalization Aggregation and enrichment Aggregation Enrichment Transformation languages and tools Examples Batch and real-time processing engines Batch processing Real-time processing Choosing between batch and real-time engines Code-driven examples Live coding example Spark implementation Imports and logging Spark session setup Load raw input data