Beschreibung Big Data Made Easy: A Working Guide to the Complete Hadoop Toolset (English Edition). 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 storage (Hadoop), configuration (YARN and ZooKeeper), collection (Nutch and Solr), processing (Storm, Pig, and Map Reduce), scheduling (Oozie), moving (Sqoop and Avro), monitoring (Chukwa, Ambari, and Hue), testing (Big Top), and analysis (Hive).The problem is that the Internet offers IT pros wading into big data many versions of the truth and some outright falsehoods born of ignorance. What is needed is a book just like this one: a wide-ranging but easily understood set of instructions to explain where to get Hadoop tools, what they can do, how to install them, how to configure them, how to integrate them, and how to use them successfully. And you need an expert who has worked in this area for a decade—someone just like author and big data expert Mike Frampton.Big Data Made Easy approaches the problem of managing massive data sets from a systems perspective, and it explains the roles for each project (like architect and tester, for example) and shows how the Hadoop toolset can be used at each system stage. It explains, in an easily understood manner and through numerous examples, how to use each tool. The book also explains the sliding scale of tools available depending upon data size and when and how to use them. Big Data Made Easy shows developers and architects, as well as testers and project managers, how to:Store big dataConfigure big dataProcess big dataSchedule processesMove data among SQL and NoSQL systemsMonitor dataPerform big data analytics Report on big data processes and projectsTest big data systemsBig Data Made Easy also explains the best part, which is that this toolset is free. Anyone can download it and—with the help of this book—start to use it within a day. With the skills this book will teach you under your belt, you will add value to your company or client immediately, not to mention your career.
Big Data Made Easy: A Working Guide to the Complete Hadoop ~ 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 storage (Hadoop), configuration (YARN and ZooKeeper), collection (Nutch and Solr), processing (Storm, Pig, and Map Reduce), scheduling (Oozie), moving (Sqoop and Avro .
9781484200957: Big Data Made Easy: A Working Guide to the ~ 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 storage (Hadoop), configuration (YARN and ZooKeeper), collection (Nutch and Solr), processing (Storm, Pig, and Map Reduce), scheduling (Oozie), moving (Sqoop and Avro .
Big Data Analytics / IBM ~ Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage and process the data with low latency. Big data has one or more of the following characteristics: high volume, high velocity or high variety. Artificial intelligence (AI), mobile, social and the Internet of Things (IoT) are driving data complexity through new .
Big Data For Dummies: : Hurwitz, Judith, Nugent ~ Leverage big data tools and architectures; Explore how big data can transform your business; Integrate structured and unstructured data into your big dataenvironment; Use predictive analytics to make better decisions; Here's the guide that can keep big data from becoming a bigheadache! Big data can be a complex concept. For Dummies to therescue .
: Field Guide to Hadoop: An Introduction to ~ Big data is over-hyped topic and Hadoop is tightly coupled with Big Data. Hadoop ecosystem is composed of many components and many of these components need good knowledge of programming (in Java or python or SQL or Scala). There are a few good books about most components. But there is no book that gives a thirty thousand feet overview of many components in Hadoop ecosystem in short and .
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Apache Hadoop ~ Tezâ„¢: A generalized data-flow programming framework, built on Hadoop YARN, which provides a powerful and flexible engine to execute an arbitrary DAG of tasks to process data for both batch and interactive use-cases. Tez is being adopted by Hiveâ„¢, Pigâ„¢ and other frameworks in the Hadoop ecosystem, and also by other commercial software (e.g. ETL tools), to replace Hadoopâ„¢ MapReduce as .
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Welcome to Apache Pig! ~ Complex tasks comprised of multiple interrelated data transformations are explicitly encoded as data flow sequences, making them easy to write, understand, and maintain. Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically, allowing the user to focus on semantics rather than efficiency.
Architecting Modern Data Platforms: A Guide to Enterprise ~ After simulating LEDs on supercomputers, he made the move from big compute in academia to big data in the public sector, first encountering Hadoop in 2012. After having fun building, deploying, managing and using Hadoop clusters, Ian joined Cloudera as a Solutions Architect in 2014. His day job now involves integrating Hadoop into enterprises and making stuff work in the real world.
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