Imagine what you could do if scalability wasn't a problem. With this hands-on guide, you’ll learn how the Cassandra database management system handles hundreds of terabytes of data while remaining highly available across multiple data centers. This expanded second edition—updated for Cassandra 3.0—provides the technical details and practical examples you need to put this database to work in a production environment. Authors Jeff Carpenter and Eben Hewitt demonstrate the advantages of Cassandra’s non-relational design, with special attention to data modeling. If you’re a developer, DBA, or application architect looking to solve a database scaling issue or future-proof your application, this guide helps you harness Cassandra’s speed and flexibility. Understand Cassandra’s distributed and decentralized structure Use the Cassandra Query Language (CQL) and cqlsh—the CQL shell Create a working data model and compare it with an equivalent relational model Develop sample applications using client drivers for languages including Java, Python, and Node.js Explore cluster topology and learn how nodes exchange data Maintain a high level of performance in your cluster Deploy Cassandra on site, in the Cloud, or with Docker Integrate Cassandra with Spark, Hadoop, Elasticsearch, Solr, and Lucene
This two volume set LNCS 8644 and LNCS 8645 constitutes the refereed proceedings of the 25th International Conference on Database and Expert Systems Applications, DEXA 2014, held in Munich, Germany, September 1-4, 2014. The 37 revised full papers presented together with 46 short papers, and 2 keynote talks, were carefully reviewed and selected from 159 submissions. The papers discuss a range of topics including: data quality; social web; XML keyword search; skyline queries; graph algorithms; information retrieval; XML; security; semantic web; classification and clustering; queries; social computing; similarity search; ranking; data mining; big data; approximations; privacy; data exchange; data integration; web semantics; repositories; partitioning; and business applications.
This two volume set LNCS 9827 and LNCS 9828 constitutes the refereed proceedings of the 27th International Conference on Database and Expert Systems Applications, DEXA 2016, held in Porto, Portugal, September 2016. The 39 revised full papers presented together with 29 short papers were carefully reviewed and selected from 137 submissions. The papers discuss a range of topics including: Temporal, Spatial, and High Dimensional Databases; Data Mining; Authenticity, Privacy, Security, and Trust; Data Clustering; Distributed and Big Data Processing; Decision Support Systems, and Learning; Data Streams; Data Integration, and Interoperability; Semantic Web, and Data Semantics; Social Networks, and Network Analysis; Linked Data; Data Analysis; NoSQL, NewSQL; Multimedia Data; Personal Information Management; Semantic Web and Ontologies; Database and Information System Architectures; Query Answering and Optimization; Information Retrieval, and Keyword Search; Data Modelling, and Uncertainty.
As our society transforms into a data-driven one, the role of the Data Scientist is becoming more and more important. If you want to be on the leading edge of what is sure to become a major profession in the not-too-distant future, this book can show you how. Each chapter is filled with practical information that will help you reap the fruits of big data and become a successful Data Scientist: • Learn what big data is and how it differs from traditional data through its main characteristics: volume, variety, velocity, and veracity. • Explore the different types of Data Scientists and the skillset each one has. • Dig into what the role of the Data Scientist requires in terms of the relevant mindset, technical skills, experience, and how the Data Scientist connects with other people. • Be a Data Scientist for a day, examining the problems you may encounter and how you tackle them, what programs you use, and how you expand your knowledge and know-how. • See how you can become a Data Scientist, based on where you are starting from: a programming, machine learning, or data-related background. • Follow step-by-step through the process of landing a Data Scientist job: where you need to look, how you would present yourself to a potential employer, and what it takes to follow a freelancer path. • Read the case studies of experienced, senior-level Data Scientists, in an attempt to get a better perspective of what this role is, in practice. At the end of the book, there is a glossary of the most important terms that have been introduced, as well as three appendices – a list of useful sites, some relevant articles on the web, and a list of offline resources for further reading.
Mastering Cloud Computing is designed for undergraduate students learning to develop cloud computing applications. Tomorrow's applications won’t live on a single computer but will be deployed from and reside on a virtual server, accessible anywhere, any time. Tomorrow's application developers need to understand the requirements of building apps for these virtual systems, including concurrent programming, high-performance computing, and data-intensive systems. The book introduces the principles of distributed and parallel computing underlying cloud architectures and specifically focuses on virtualization, thread programming, task programming, and map-reduce programming. There are examples demonstrating all of these and more, with exercises and labs throughout. Explains how to make design choices and tradeoffs to consider when building applications to run in a virtual cloud environment Real-world case studies include scientific, business, and energy-efficiency considerations
Author: Albert Y. Zomaya
Release Date: 2017-02-25
This handbook offers comprehensive coverage of recent advancements in Big Data technologies and related paradigms. Chapters are authored by international leading experts in the field, and have been reviewed and revised for maximum reader value. The volume consists of twenty-five chapters organized into four main parts. Part one covers the fundamental concepts of Big Data technologies including data curation mechanisms, data models, storage models, programming models and programming platforms. It also dives into the details of implementing Big SQL query engines and big stream processing systems. Part Two focuses on the semantic aspects of Big Data management including data integration and exploratory ad hoc analysis in addition to structured querying and pattern matching techniques. Part Three presents a comprehensive overview of large scale graph processing. It covers the most recent research in large scale graph processing platforms, introducing several scalable graph querying and mining mechanisms in domains such as social networks. Part Four details novel applications that have been made possible by the rapid emergence of Big Data technologies such as Internet-of-Things (IOT), Cognitive Computing and SCADA Systems. All parts of the book discuss open research problems, including potential opportunities, that have arisen from the rapid progress of Big Data technologies and the associated increasing requirements of application domains. Designed for researchers, IT professionals and graduate students, this book is a timely contribution to the growing Big Data field. Big Data has been recognized as one of leading emerging technologies that will have a major contribution and impact on the various fields of science and varies aspect of the human society over the coming decades. Therefore, the content in this book will be an essential tool to help readers understand the development and future of the field.
This book constitutes the refereed proceedings of the 17th International Conference on Model Driven Engineering Languages and Systems, MODELS 2014, held in Valencia, Spain, in September/October 2014. The 41 full papers presented in this volume were carefully reviewed and selected from a total of 126 submissions. The scope of the conference series is broad, encompassing modeling languages, methods, tools, and applications considered from theoretical and practical angles and in academic and industrial settings. The papers report on the use of modeling in a wide range of cloud, mobile, and web computing, model transformation behavioral modeling, MDE: past, present, future, formal semantics, specification, and verification, models at runtime, feature and variability modeling, composition and adaptation, practices and experience, modeling for analysis, pragmatics, model extraction, manipulation and persistence, querying, and reasoning.
This book constitutes the refereed proceedings of the 6th IFIP WG 6.6 International Conference on Autonomous Infrastructure, Management, and Security, AIMS 2012, held in Luxembourg in June 2012. The 10 full papers presented were carefully reviewed and selected from 23 submissions. They cover autonomic and distributed management, network security, network monitoring, and special environments and Internet of Things. In addition, this book contains 9 workshop papers which were selected from 18 submissions. They deal with high-speed networks and network management, intrusion detection, and network monitoring and security.