Practical Dataflow Engineering
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Practical Dataflow Engineering
"Practical Dataflow Engineering" "Practical Dataflow Engineering" is a comprehensive guide to the theory, architecture, and practice of building resilient and scalable dataflow systems. Beginning with foundational concepts, the book traces the evolution of dataflow models from their historical roots to their critical role in modern computation. Readers will gain a deep understanding of the mathematical abstractions, such as directed acyclic graphs and token-based computation, that underpin effective dataflow design, as well as the nuances of synchronous and asynchronous execution. These fundamentals are seamlessly connected to the trends in functional programming, event-driven computation, and stream processing that shape contemporary data systems. Through accessible yet thorough chapters, the book examines architectural patterns essential for real-world dataflow applications. It addresses core topics including pipeline and DAG orchestrations, windowing for temporal data, stateful versus stateless processing, and advanced techniques for join, aggregation, and fault tolerance. Readers are introduced to distributed dataflow infrastructure, covering load balancing, checkpointing, network protocols, and cloud-native deployment—all with a keen focus on elasticity, federated architecture, and edge computing. Practical programming guidance is provided for major frameworks like Apache Beam, Flink, and Spark Structured Streaming, alongside strategies for operator development, composable API design, and advanced transformation patterns. Moving beyond system design, "Practical Dataflow Engineering" equips professionals with actionable insights into the optimization, observability, and operational excellence required for reliable production systems. The book covers end-to-end topics such as latency and throughput tuning, memory and resource management, secure communication, regulatory compliance, and multi-tenant architecture. Advanced sections explore dataflow's intersection with AI, serverless technologies, and the future of distributed computation, making this work an essential resource for data engineers, architects, and software developers striving to deliver high-impact, future-ready data solutions.
NiFi Dataflow Engineering
"NiFi Dataflow Engineering" "NiFi Dataflow Engineering" is a comprehensive guide to designing, implementing, and operating sophisticated data pipelines using Apache NiFi. The book is meticulously structured to take you from foundational concepts—such as NiFi’s architecture, flow-based programming principles, and its powerful abstractions—through to advanced dataflow patterns and best practices. Whether you’re new to NiFi or seeking to master the intricacies of process groups, repositories, controller services, and flow versioning, this resource offers deep insights into creating modular, maintainable, and scalable dataflows. Beyond fundamentals, the book delves into advanced engineering patterns essential for real-world deployments. Readers will discover strategies for building robust, high-throughput flows with dynamic routing, prioritization, and error management, as well as techniques for developing custom processors and services. Coverage of operationalization in production environments addresses clustering, high availability, security, audit logging, disaster recovery, and continuous integration/continuous deployment (CI/CD)—making it invaluable for engineers tasked with large-scale, mission-critical data workloads. Finally, "NiFi Dataflow Engineering" explores integration with major data ecosystem tools, including Hadoop, Kafka, cloud platforms, and governance solutions, ensuring connectivity across modern data architectures. The book closes with forward-looking chapters on trends such as real-time analytics, IoT, edge processing, machine learning orchestration, serverless dataflows, automation, and self-healing pipelines. This makes it an essential reference for professionals aspiring to leverage Apache NiFi as the backbone of agile, secure, and transformative digital enterprises.
Data Engineering on the Cloud: A Practical Guide 2025
Author: Raghu Gopa, Dr. Arpita Roy
language: en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date:
PREFACE The digital transformation of businesses and the exponential growth of data have created a fundamental shift in how organizations approach data management, analytics, and decision-making. As cloud technologies continue to evolve, cloud-based data engineering has become central to the success of modern data-driven enterprises. “Data Engineering on the Cloud: A Practical Guide” aims to equip data professionals, engineers, and organizations with the knowledge and practical tools needed to build and manage scalable, secure, and efficient data engineering pipelines in cloud environments. This book is designed to bridge the gap between the theoretical foundations of data engineering and the practical realities of working with cloud-based data platforms. Cloud computing has revolutionized data storage, processing, and analytics by offering unparalleled scalability, flexibility, and cost efficiency. However, with these opportunities come new challenges, including selecting the right tools, architectures, and strategies to ensure seamless data integration, transformation, and delivery. As businesses increasingly migrate their data to the cloud, it is essential for data engineers to understand how to leverage the capabilities of the cloud to build robust data pipelines that can handle large, complex datasets in real-time. Throughout this guide, we will explore the various facets of cloud-based data engineering, from understanding cloud storage and computing services to implementing data integration techniques, managing data quality, and optimizing performance. Whether you are building data pipelines from scratch, migrating on-premises systems to the cloud, or enhancing existing data workflows, this book will provide actionable insights and step-by-step guidance on best practices, tools, and frameworks commonly used in cloud data engineering. Key topics covered in this book include: · The fundamentals of cloud architecture and the role of cloud providers (such as AWS, Google Cloud, and Microsoft Azure) in data engineering workflows. · Designing scalable and efficient data pipelines using cloud-based tools and services. · Integrating diverse data sources, including structured, semi-structured, and unstructured data, for seamless processing and analysis. · Data transformation techniques, including ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform), in cloud environments. · Ensuring data quality, governance, and security when working with cloud data platforms. · Optimizing performance for data storage, processing, and analytics to handle growing data volumes and complexity. This book is aimed at professionals who are already familiar with data engineering concepts and are looking to apply those concepts within cloud environments. It is also suitable for organizations that are in the process of migrating to cloud-based data platforms and wish to understand the nuances and best practices for cloud data engineering. In addition to theoretical knowledge, this guide emphasizes hands-on approaches, providing practical examples, code snippets, and real-world case studies to demonstrate the effective implementation of cloud-based data engineering solutions. We will explore how to utilize cloud-native services to streamline workflows, improve automation, and reduce manual interventions in data pipelines. Throughout the book, you will gain insights into the evolving tools and technologies that make data engineering more agile, reliable, and efficient. The role of data engineering is growing ever more important in enabling businesses to unlock the value of their data. By the end of this book, you will have a comprehensive understanding of how to leverage cloud technologies to build high-performance, scalable data engineering solutions that are aligned with the needs of modern data-driven organizations. We hope this guide helps you to navigate the complexities of cloud data engineering and helps you unlock new possibilities for your data initiatives. Welcome to “Data Engineering on the Cloud: A Practical Guide.” Let’s embark on this journey to harness the full potential of cloud technologies in the world of data engineering. Authors