Designing and writing a real-time streaming publication with Apache Apex About This Book Get a clear, practical approach to real-time data processing Program Apache Apex streaming applications This book shows you Apex integration with the open source Big Data ecosystem Who This Book Is For This book assumes knowledge of application development with Java and familiarity with distributed systems. Familiarity with other real-time streaming frameworks is not required, but some practical experience with other big data processing utilities might be helpful. What You Will Learn Put together a functioning Apex application from scratch Scale an Apex application and configure it for optimal performance Understand how to deal with failures via the fault tolerance features of the platform Use Apex via other frameworks such as Beam Understand the DevOps implications of deploying Apex In Detail Apache Apex is a next-generation stream processing framework designed to operate on data at large scale, with minimum latency, maximum reliability, and strict correctness guarantees. Half of the book consists of Apex applications, showing you key aspects of data processing pipelines such as connectors for sources and sinks, and common data transformations. The other half of the book is evenly split into explaining the Apex framework, and tuning, testing, and scaling Apex applications. Much of our economic world depends on growing streams of data, such as social media feeds, financial records, data from mobile devices, sensors and machines (the Internet of Things - IoT). The projects in the book show how to process such streams to gain valuable, timely, and actionable insights. Traditional use cases, such as ETL, that currently consume a significant chunk of data engineering resources are also covered. The final chapter shows you future possibilities emerging in the streaming space, and how Apache Apex can contribute to it. Style and approach This book is divided into two major parts: first it explains what Apex is, what its relevant parts are, and how to write well-built Apex applications. The second part is entirely application-driven, walking you through Apex applications of increasing complexity.

Designing and writing a real-time streaming publication with Apache ApexAbout This Book* Get a clear, practical approach to real-time data processing* Program Apache Apex streaming applications* This book shows you Apex integration with the open source Big Data ecosystemWho This Book Is ForThis book assumes knowledge of application development with Java and familiarity with distributed systems. Familiarity with other real-time streaming frameworks is not required, but some practical experience with other big data processing utilities might be helpful.What You Will Learn* Put together a functioning Apex application from scratch* Scale an Apex application and configure it for optimal performance* Understand how to deal with failures via the fault tolerance features of the platform* Use Apex via other frameworks such as Beam* Understand the DevOps implications of deploying ApexIn DetailApache Apex is a next-generation stream processing framework designed to operate on data at large scale, with minimum latency, maximum reliability, and strict correctness guarantees.Half of the book consists of Apex applications, showing you key aspects of data processing pipelines such as connectors for sources and sinks, and common data transformations. The other half of the book is evenly split into explaining the Apex framework, and tuning, testing, and scaling Apex applications.Much of our economic world depends on growing streams of data, such as social media feeds, financial records, data from mobile devices, sensors and machines (the Internet of Things - IoT). The projects in the book show how to process such streams to gain valuable, timely, and actionable insights. Traditional use cases, such as ETL, that currently consume a significant chunk of data engineering resources are also covered.The final chapter shows you future possibilities emerging in the streaming space, and how Apache Apex can contribute to it.Style and approachThis book is divided into two major parts: first it explains what Apex is, what its relevant parts are, and how to write well-built Apex applications. The second part is entirely application-driven, walking you through Apex applications of increasing complexity.

Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform. Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments. Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP. What You’ll Learn Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your resultsKnow the programming concepts relevant to machine and deep learning design and development using the Python stack Build and interpret machine and deep learning models Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products Be aware of the different facets and design choices to consider when modeling a learning problem Productionalize machine learning models into software products Who This Book Is For Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers

There’s growing interest in learning how to analyze streaming data in large-scale systems such as web traffic, financial transactions, machine logs, industrial sensors, and many others. But analyzing data streams at scale has been difficult to do well—until now. This practical book delivers a deep introduction to Apache Flink, a highly innovative open source stream processor with a surprising range of capabilities. Authors Ellen Friedman and Kostas Tzoumas show technical and nontechnical readers alike how Flink is engineered to overcome significant tradeoffs that have limited the effectiveness of other approaches to stream processing. You’ll also learn how Flink has the ability to handle both stream and batch data processing with one technology. Learn the consequences of not doing streaming well—in retail and marketing, IoT, telecom, and banking and finance Explore how to design data architecture to gain the best advantage from stream processing Get an overview of Flink’s capabilities and features, along with examples of how companies use Flink, including in production Take a technical dive into Flink, and learn how it handles time and stateful computation Examine how Flink processes both streaming (unbounded) and batch (bounded) data without sacrificing performance

Engineering education methods and standards are important features of engineering programs that should be carefully designed both to provide students and stakeholders with valuable, active, integrated learning experiences, and to provide a vehicle for assessing program outcomes. With the driving force of the globalization of the engineering profession, standards should be developed for mutual recognition of engineering education across the world, but it is proving difficult to achieve. The Handbook of Research on Engineering Education in a Global Context provides innovative insights into the importance of quality training and preparation for engineering students. It explores the common and current problems encountered in areas such as quality and standards, management information systems, innovation and enhanced learning technologies in education, as well as the challenges of employability, entrepreneurship, and diversity. This publication is vital reference source for science and engineering educators, engineering professionals, and educational administrators interested in topics centered on the education of students in the field of engineering.

This book primarily aims to provide an in-depth understanding of recent advances in big data computing technologies, methodologies, and applications along with introductory details of big data computing models such as Apache Hadoop, MapReduce, Hive, Pig, Mahout in-memory storage systems, NoSQL databases, and big data streaming services such as Apache Spark, Kafka, and so forth. It also covers developments in big data computing applications such as machine learning, deep learning, graph processing, and many others. Features: Provides comprehensive analysis of advanced aspects of big data challenges and enabling technologies. Explains computing models using real-world examples and dataset-based experiments. Includes case studies, quality diagrams, and demonstrations in each chapter. Describes modifications and optimization of existing technologies along with the novel big data computing models. Explores references to machine learning, deep learning, and graph processing. This book is aimed at graduate students and researchers in high-performance computing, data mining, knowledge discovery, and distributed computing.

It’s time to get your head in the cloud! In today’s business environment, more and more people are requesting cloud-based solutions to help solve their business challenges. So how can you not only anticipate your clients’ needs but also keep ahead of the curve to ensure their goals stay on track? With the help of this accessible book, you’ll get a clear sense of cloud computing and understand how to communicate the benefits, drawbacks, and options to your clients so they can make the best choices for their unique needs. Plus, case studies give you the opportunity to relate real-life examples of how the latest technologies are giving organizations worldwide the opportunity to thrive as supply chain solutions in the cloud. Demonstrates how improvements in forecasting, collaboration, and inventory optimization can lead to cost savings Explores why cloud computing is becoming increasingly important Takes a close look at the types of cloud computing Makes sense of demand-driven forecasting using Amazon's cloud Whether you work in management, business, or IT, this is the dog-eared reference you’ll want to keep close by as you continue making sense of the cloud.