Processing Data
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| Author | : Svilen Gospodinov |
| Publisher | : Pragmatic Bookshelf |
| Total Pages | : 221 |
| Release | : 2021-07-25 |
| Genre | : Computers |
| ISBN | : 1680508962 |
Learn different ways of writing concurrent code in Elixir and increase your application's performance, without sacrificing scalability or fault-tolerance. Most projects benefit from running background tasks and processing data concurrently, but the world of OTP and various libraries can be challenging. Which Supervisor and what strategy to use? What about GenServer? Maybe you need back-pressure, but is GenStage, Flow, or Broadway a better choice? You will learn everything you need to know to answer these questions, start building highly concurrent applications in no time, and write code that's not only fast, but also resilient to errors and easy to scale. Whether you are building a high-frequency stock trading application or a consumer web app, you need to know how to leverage concurrency to build applications that are fast and efficient. Elixir and the OTP offer a range of powerful tools, and this guide will show you how to choose the best tool for each job, and use it effectively to quickly start building highly concurrent applications. Learn about Tasks, supervision trees, and the different types of Supervisors available to you. Understand why processes and process linking are the building blocks of concurrency in Elixir. Get comfortable with the OTP and use the GenServer behaviour to maintain process state for long-running jobs. Easily scale the number of running processes using the Registry. Handle large volumes of data and traffic spikes with GenStage, using back-pressure to your advantage. Create your first multi-stage data processing pipeline using producer, consumer, and producer-consumer stages. Process large collections with Flow, using MapReduce and more in parallel. Thanks to Broadway, you will see how easy it is to integrate with popular message broker systems, or even existing GenStage producers. Start building the high-performance and fault-tolerant applications Elixir is famous for today. What You Need: You'll need Elixir 1.9+ and Erlang/OTP 22+ installed on a Mac OS X, Linux, or Windows machine.
| Author | : Gauri Misra |
| Publisher | : Academic Press |
| Total Pages | : 191 |
| Release | : 2019-03-23 |
| Genre | : Science |
| ISBN | : 0128172800 |
Data Processing Handbook for Complex Biological Data provides relevant and to the point content for those who need to understand the different types of biological data and the techniques to process and interpret them. The book includes feedback the editor received from students studying at both undergraduate and graduate levels, and from her peers. In order to succeed in data processing for biological data sources, it is necessary to master the type of data and general methods and tools for modern data processing. For instance, many labs follow the path of interdisciplinary studies and get their data validated by several methods. Researchers at those labs may not perform all the techniques themselves, but either in collaboration or through outsourcing, they make use of a range of them, because, in the absence of cross validation using different techniques, the chances for acceptance of an article for publication in high profile journals is weakened. - Explains how to interpret enormous amounts of data generated using several experimental approaches in simple terms, thus relating biology and physics at the atomic level - Presents sample data files and explains the usage of equations and web servers cited in research articles to extract useful information from their own biological data - Discusses, in detail, raw data files, data processing strategies, and the web based sources relevant for data processing
| Author | : Linda Brookover Bourque |
| Publisher | : SAGE |
| Total Pages | : 102 |
| Release | : 1992-06-06 |
| Genre | : Science |
| ISBN | : 9780803947412 |
This volume highlights the theory that decisions made during the design of a data collection instrument influence the kind of data and the format of the data that are available for analysis. Opening with a discussion on the selection of the data collection technique(s) and how this impacts on data processing and the data for later analysis, the book covers key issues such as: should you create your own instrument for a questionnaire? how do you test a questionnaire? what are the characteristics of good data processing? how to deal with missing data? how to scale an evaluation and create subfiles for analysis? In addition, each major section concludes with examples and when appropriate, directs the reader to commonly available computer software that can aid in data processing.
| Author | : Sherif Sakr |
| Publisher | : CRC Press |
| Total Pages | : 640 |
| Release | : 2014-06-25 |
| Genre | : Computers |
| ISBN | : 1466581506 |
Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments. The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-based deployment models. The book’s second section examines the usage of advanced Big Data processing techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big Data processing such as consistency management, privacy, and security. Supplying a comprehensive summary from both the research and applied perspectives, the book covers recent research discoveries and applications, making it an ideal reference for a wide range of audiences, including researchers and academics working on databases, data mining, and web scale data processing. After reading this book, you will gain a fundamental understanding of how to use Big Data-processing tools and techniques effectively across application domains. Coverage includes cloud data management architectures, big data analytics visualization, data management, analytics for vast amounts of unstructured data, clustering, classification, link analysis of big data, scalable data mining, and machine learning techniques.
| Author | : Susan Wooldridge |
| Publisher | : Elsevier |
| Total Pages | : 272 |
| Release | : 2013-10-22 |
| Genre | : Technology & Engineering |
| ISBN | : 1483105245 |
Data Processing: Made Simple, Second Edition presents discussions of a number of trends and developments in the world of commercial data processing. The book covers the rapid growth of micro- and mini-computers for both home and office use; word processing and the 'automated office'; the advent of distributed data processing; and the continued growth of database-oriented systems. The text also discusses modern digital computers; fundamental computer concepts; information and data processing requirements of commercial organizations; and the historical perspective of the computer industry. The computer hardware and software and the development and implementation of a computer system are considered. The book tackles careers in data processing; the tasks carried out by the data processing department; and the way in which the data processing department fits in with the rest of the organization. The text concludes by examining some of the problems of running a data processing department, and by suggesting some possible solutions. Computer science students will find the book invaluable.
| Author | : Srini Penchikala |
| Publisher | : Lulu.com |
| Total Pages | : 106 |
| Release | : 2018-03-13 |
| Genre | : Computers |
| ISBN | : 1387659952 |
Apache Spark is a popular open-source big-data processing framework thatÕs built around speed, ease of use, and unified distributed computing architecture. Not only it supports developing applications in different languages like Java, Scala, Python, and R, itÕs also hundred times faster in memory and ten times faster even when running on disk compared to traditional data processing frameworks. Whether you are currently working on a big data project or interested in learning more about topics like machine learning, streaming data processing, and graph data analytics, this book is for you. You can learn about Apache Spark and develop Spark programs for various use cases in big data analytics using the code examples provided. This book covers all the libraries in Spark ecosystem: Spark Core, Spark SQL, Spark Streaming, Spark ML, and Spark GraphX.
| Author | : Jimmy Lin |
| Publisher | : Springer Nature |
| Total Pages | : 171 |
| Release | : 2022-05-31 |
| Genre | : Computers |
| ISBN | : 3031021363 |
Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks
| Author | : Gary B. Shelly |
| Publisher | : Brooks/Cole |
| Total Pages | : 516 |
| Release | : 1980 |
| Genre | : Computers |
| ISBN | : |
Alberta Authorized Resource for grade 10-12 ca 1980-1997.
| Author | : Valentina Janev |
| Publisher | : Springer Nature |
| Total Pages | : 212 |
| Release | : 2020-07-15 |
| Genre | : Computers |
| ISBN | : 3030531996 |
This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.
| Author | : James Densmore |
| Publisher | : O'Reilly Media |
| Total Pages | : 277 |
| Release | : 2021-02-10 |
| Genre | : Computers |
| ISBN | : 1492087807 |
Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting
