Practical Dataflow Engineering
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Practical Dataflow Engineering
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-15
Practical Dataflow Engineering written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-15 with Computers categories.
"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.
Data Flow Analysis
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Author : Uday Khedker
language : en
Publisher: CRC Press
Release Date : 2017-12-19
Data Flow Analysis written by Uday Khedker and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-19 with Computers categories.
Data flow analysis is used to discover information for a wide variety of useful applications, ranging from compiler optimizations to software engineering and verification. Modern compilers apply it to produce performance-maximizing code, and software engineers use it to re-engineer or reverse engineer programs and verify the integrity of their programs. Supplementary Online Materials to Strengthen Understanding Unlike most comparable books, many of which are limited to bit vector frameworks and classical constant propagation, Data Flow Analysis: Theory and Practice offers comprehensive coverage of both classical and contemporary data flow analysis. It prepares foundations useful for both researchers and students in the field by standardizing and unifying various existing research, concepts, and notations. It also presents mathematical foundations of data flow analysis and includes study of data flow analysis implantation through use of the GNU Compiler Collection (GCC). Divided into three parts, this unique text combines discussions of inter- and intraprocedural analysis and then describes implementation of a generic data flow analyzer (gdfa) for bit vector frameworks in GCC. Through the inclusion of case studies and examples to reinforce material, this text equips readers with a combination of mutually supportive theory and practice, and they will be able to access the author’s accompanying Web page. Here they can experiment with the analyses described in the book, and can make use of updated features, including: Slides used in the authors’ courses The source of the generic data flow analyzer (gdfa) An errata that features errors as they are discovered Additional updated relevant material discovered in the course of research
Nifi Dataflow Engineering
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-08
Nifi Dataflow Engineering written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-08 with Computers categories.
"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 With Google Cloud Platform
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Author : Adi Wijaya
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-03-31
Data Engineering With Google Cloud Platform written by Adi Wijaya and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-31 with Computers categories.
Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineer Key Features Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines Discover tips to prepare for and pass the Professional Data Engineer exam Book DescriptionWith this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.What you will learn Load data into BigQuery and materialize its output for downstream consumption Build data pipeline orchestration using Cloud Composer Develop Airflow jobs to orchestrate and automate a data warehouse Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster Leverage Pub/Sub for messaging and ingestion for event-driven systems Use Dataflow to perform ETL on streaming data Unlock the power of your data with Data Studio Calculate the GCP cost estimation for your end-to-end data solutions Who this book is for This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book.
A Practical Introduction To Hardware Software Codesign
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Author : Patrick R. Schaumont
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-09-09
A Practical Introduction To Hardware Software Codesign written by Patrick R. Schaumont and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09-09 with Technology & Engineering categories.
This is a practical book for computer engineers who want to understand or implement hardware/software systems. It focuses on problems that require one to combine hardware design with software design – such problems can be solved with hardware/software codesign. When used properly, hardware/software co- sign works better than hardware design or software design alone: it can improve the overall performance of digital systems, and it can shorten their design time. Hardware/software codesign can help a designer to make trade-offs between the ?exibility and the performanceof a digital system. To achieve this, a designer needs to combine two radically different ways of design: the sequential way of dec- position in time, using software, with the parallel way of decomposition in space, using hardware. Intended Audience This book assumes that you have a basic understandingof hardware that you are - miliar with standard digital hardware componentssuch as registers, logic gates, and components such as multiplexers and arithmetic operators. The book also assumes that you know how to write a program in C. These topics are usually covered in an introductory course on computer engineering or in a combination of courses on digital design and software engineering.
Google Cloud Professional Data Engineer Exam Practice Questions And Dumps
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Author : Zoom Books
language : en
Publisher: Zoom Books
Release Date :
Google Cloud Professional Data Engineer Exam Practice Questions And Dumps written by Zoom Books and has been published by Zoom Books this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
A Professional Data Engineer authorize data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to blueprint, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuous train pre-existing machine learning models. Here we’ve brought best Exam practice questions for Google Cloud so that you can prepare well for Professional Data Engineer exam. Unlike other online simulation practice tests, you get an eBook version that is easy to read & remember these questions. You can simply rely on these questions for successfully certifying this exam.
Data Engineering On The Cloud A Practical Guide 2025
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Author : Raghu Gopa, Dr. Arpita Roy
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :
Data Engineering On The Cloud A Practical Guide 2025 written by Raghu Gopa, Dr. Arpita Roy and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
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
Privacy Engineering
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Author : Ian Oliver
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2014-07-18
Privacy Engineering written by Ian Oliver and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-18 with Computer security categories.
Information privacy is the major defining issue of today's Internet enabled World. To construct information systems from small mobile 'apps' to huge, heterogeneous, cloudified systems requires merging together skills from software engineering, legal, security and many other disciplines - including some outside of these fields! Only through properly modelling the system under development can we full appreciate the complexity of where personal data and information flows; and more importantly, effectively communicate this.This book presents an approach based upon data flow modelling, coupled with standardised terminological frameworks, classifications and ontologies to properly annotate and describe the flow of information into, out of and across these systems. Also provided are structures and frameworks for the engineering process, requirements and audits; and even the privacy programme itself, but takes a pragmatic approach and encourages using and modifying the tools and techniques presented as the local context and needs require.
Google Cloud Platform Gcp Associate Cloud Engineer Ace Practice Tests Exams 179 Questions Answers Pdf
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Author : Daniel Danielecki
language : en
Publisher: Daniel Danielecki
Release Date : 2023-06-09
Google Cloud Platform Gcp Associate Cloud Engineer Ace Practice Tests Exams 179 Questions Answers Pdf written by Daniel Danielecki and has been published by Daniel Danielecki this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-09 with Computers categories.
⌛️ Short and to the point; why should you buy the PDF with these Practice Tests Exams: 1. Always happy to answer your questions on Google Play Books and outside :) 2. Failed? Please submit a screenshot of your exam result and request a refund; we'll always accept it. 3. Learn about topics, such as: - BigQuery; - Billing Administrator; - Cloud Audit; - Cloud Bigtable; - Cloud Concepts; - Cloud Dataflow; - Cloud Datastore; - Cloud Identity and Access Management (Cloud IAM); - Cloud Logging; - Cloud Pub/Sub; - Cloud Run; - Cloud SDK; - Cloud Shell; - Cloud Spanner; - Cloud SQL; - Cloud Storage; - Coldline Storage; - Compute Engine; - Deployment Manager; - Google Cloud Platform Console (GCP Console); - Google App Engine; - Google Cloud Marketplace; - Google Kubernetes Engine (GKE); - Nearline Storage; - Project Billing Manager; - Stackdriver; - Virtual Private Cloud (VPC); - Much More! 4. Questions are similar to the actual exam, without duplications (like in other practice exams ;-)). 5. These tests are not a Google Cloud Platform (GCP) Associate Cloud Engineer (ACE) Exam Dump. Some people use brain dumps or exam dumps, but that's absurd, which we don't practice. 6. 179 unique questions.
Multiprocessing In Meteorological Models
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Author : Geerd-R. Hoffmann
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Multiprocessing In Meteorological Models written by Geerd-R. Hoffmann and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Science categories.
Numerical weather prediction on the one hand needs a very large number of floating point calculations, but on the other hand is very time-critical. Therefore, the largest computers available, i.e., the "supercomputers", have usually been acquired by the national meteorological services long before they were used in other fields of research or business. Since the available technology limits the speed of any single computer, parallel computations have become necessary to achieve further improvements in the number of results produced per time unit. This book collects the papers presented at two workshops held at ECMWF on the topic of parallel processing in meteorological models. It provides an insight into the state-of-the-art in using parallel processors operationally and allows extrapolation to other time-critical applications. It also shows trends in migrating to massive parallel systems in the near future.