See a Mesos-based big data stack created and the components used. You will use currently available Apache full and incubating systems. The components are introduced by example and you learn how they work together.In the Complete Guide to Open Source Big Data Stack, the author begins by creating a private cloud and then installs and examines Apache Brooklyn. After that, he uses each chapter to introduce one piece of the big data stack—sharing how to source the software and ...

Many corporations are finding that the size of their data sets are outgrowing the capability of their systems to store and process them. The data is becoming too big to manage and use with traditional tools. The solution: implementing a big data system.As Big Data Made Easy: A Working Guide to the Complete Hadoop Toolset shows, Apache Hadoop offers a scalable, fault-tolerant system for storing and processing data in parallel. It has a very rich toolset that allows for stor...

Entity Framework 6 Recipes provides an exhaustive collection of ready-to-use code solutions for Entity Framework, Microsoft's model-centric, data-access platform for the .NET Framework and ASP.NET development. With this book, you will learn the core concepts of Entity Framework through a broad range of clear and concise solutions to everyday data access tasks. Armed with this experience, you will be ready to dive deep into Entity Framework, experiment with new approaches, and deve...

An easy-to-use guide, full of hands-on recipes for manipulating spatial data in a PostGIS database. Each topic is explained and placed in context, and for the more inquisitive, there are more details of the concepts used. If you are a web developer or a software architect, especially in location-based companies, and want to expand the range of techniques you are using with PostGIS, then this book is for you. You should have some prior experience with PostgreSQL database and spatial concept...

Big Data Analytics Using Splunk is a hands-on book showing how to process and derive business value from big data in real time. Examples in the book draw from social media sources such as Twitter (tweets) and Foursquare (check-ins). You also learn to draw from machine data, enabling you to analyze, say, web server log files and patterns of user access in real time, as the access is occurring. Gone are the days when you need be caught out by shifting public opinion or sudden change...

PL/SQL, Oracle's powerful procedural language, has been the cornerstone of Oracle application development for nearly 15 years. Although primarily a tool for developers, PL/SQL has also become an essential tool for database administration, as DBAs take increasing responsibility for site performance and as the lines between developers and DBAs blur.Until now, there has not been a book focused squarely on the language topics of special concern to DBAs Oracle PL/SQL for DBAs f...

This book takes a concentrated look at the very latest best practices for Office 365 migration, with a focus on the needs of senior managers, IT managers, and others involved in key decisions when moving their business to the cloud.Based on popular chapters in his first book, Office 365: Migrating and Managing Your Business in the Cloud (Apress Open, co-authored with Don Crawford), author Matt Katzer revises and expands on his original material to bring yo...

This Springer Brief provides a comprehensive overview of the background and recent developments of big data. The value chain of big data is divided into four phases: data generation, data acquisition, data storage and data analysis. For each phase, the book introduces the general background, discusses technical challenges and reviews the latest advances. Technologies under discussion include cloud computing, Internet of Things, data centers, Hadoop and more. The authors also explore severa...

The issue of data quality is as old as data itself. However, the proliferation of diverse, large-scale and often publically available data on the Web has increased the risk of poor data quality and misleading data interpretations. On the other hand, data is now exposed at a much more strategic level e.g. through business intelligence systems, increasing manifold the stakes involved for individuals, corporations as well as government agencies. There, the lack of knowledge about data accurac...

Big Data Imperatives, focuses on resolving the key questions on everyone’s mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications?Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its ef...

Learn how to integrate full-stack open source big data architecture and to choose the correct technology—Scala/Spark, Mesos, Akka, Cassandra, and Kafka—in every layer.Big data architecture is becoming a requirement for many different enterprises. So far, however, the focus has largely been on collecting, aggregating, and crunching large data sets in a timely manner. In many cases now, organizations need more than one paradigm to perform efficient analyses.Big Data SMACK...

QoS, short for "quality of service, is one of the most important goals a network designer or administrator will have. Ensuring that the network runs at optimal precision with data remaining accurate, traveling fast, and to the correct user are the main objectives of QoS. The various media that fly across the network including voice, video, and data have different idiosyncrasies that try the dimensions of the network. This malleable network architecture poses an always moving potential prob...