Big Data Made Easy: A Working Guide to the Complete Hadoop by Michael Frampton

By Michael Frampton

Many organizations are discovering that the scale in their facts units are outgrowing the aptitude in their platforms to shop and procedure them. the knowledge is turning into too huge to regulate and use with conventional instruments. the answer: imposing a major facts system.

As enormous information Made effortless: A operating advisor to the whole Hadoop Toolset indicates, Apache Hadoop deals a scalable, fault-tolerant procedure for storing and processing information in parallel. It has a truly wealthy toolset that enables for garage (Hadoop), configuration (YARN and ZooKeeper), assortment (Nutch and Solr), processing (Storm, Pig, and Map Reduce), scheduling (Oozie), relocating (Sqoop and Avro), tracking (Chukwa, Ambari, and Hue), checking out (Big Top), and research (Hive).

The challenge is that the net deals IT execs wading into substantial info many types of the reality and a few outright falsehoods born of lack of knowledge. what's wanted is a booklet similar to this one: a wide-ranging yet simply understood set of directions to give an explanation for the place to get Hadoop instruments, what they could do, the right way to set up them, easy methods to configure them, the way to combine them, and the way to exploit them effectively. and also you want a professional who has labored during this sector for a decade—someone similar to writer and large info specialist Mike Frampton.

Big info Made Easy methods the matter of dealing with titanic information units from a structures viewpoint, and it explains the jobs for every undertaking (like architect and tester, for instance) and exhibits how the Hadoop toolset can be utilized at every one approach degree. It explains, in an simply understood demeanour and during various examples, the best way to use each one instrument. The publication additionally explains the sliding scale of instruments to be had based upon info dimension and whilst and the way to exploit them. Big info Made Easy indicates builders and designers, in addition to testers and undertaking managers, how to:

  • Store gigantic data
  • Configure massive data
  • Process vast data
  • Schedule processes
  • Move info between SQL and NoSQL systems
  • Monitor data
  • Perform substantial information analytics
  • Report on substantial info approaches and projects
  • Test substantial info systems

Big information Made Easy additionally explains the easiest half, that's that this toolset is loose. a person can obtain it and—with the aid of this book—start to exploit it inside an afternoon. With the abilities this e-book will educate you below your belt, you are going to upload price for your corporation or shopper instantly, let alone your career.

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Additional resources for Big Data Made Easy: A Working Guide to the Complete Hadoop Toolset

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The Task Tracker user interface Now that you have tasted the flavor of Hadoop V1, shut it down and get ready to install Hadoop V2. Hadoop V2 Installation In moving on to Hadoop V2, you will this time download and use the Cloudera stack. Specifically, you will install CDH 4 because it is available for both 32-bit and 64-bit machines and it supports YARN. I have chosen to install the latest manual CDH release available at the time of this writing. In this section, you will not only learn how to obtain and install the Cloudera Hadoop packages; you’ll also find out how to install, run, and use ZooKeeper, as well as how to configure Hadoop V2.

33 percent into its Reduce phase. 30 Chapter 2 ■ Storing and Configuring Data with Hadoop, YARN, and ZooKeeper Figure 2-4. xml. Use the URL http://hc1nn:50060/ (on the name node hc1nn) to access it and check the status of current tasks. Figure 2-5 shows running and non-running tasks, as well as providing a link to the log files. It also offers a basic list of task statuses and their progress. 31 Chapter 2 ■ Storing and Configuring Data with Hadoop, YARN, and ZooKeeper Figure 2-5. The Task Tracker user interface Now that you have tasted the flavor of Hadoop V1, shut it down and get ready to install Hadoop V2.

You can recursively delete in HDFS by using rm -r: [hadoop@hc1nn ~]$ hadoop fs -rm -r /test [hadoop@hc1nn ~]$ hadoop fs -ls / Found 4 items drwxrwxrwt - hdfs hadoop 0 2014-03-23 14:58 /tmp drwxr-xr-x - hdfs hadoop 0 2014-03-23 16:06 /user drwxr-xr-x - hdfs hadoop 0 2014-03-23 14:56 /var The example above has deleted the HDFS directory /test and all of its contents. 5 M /user 0 /var The -h option just makes the numbers humanly readable. This last example shows that only the HDFS file system /user directory is using any space.

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