By Michael Frampton
Many enterprises are discovering that the scale in their information units are outgrowing the aptitude in their structures to shop and method them. the knowledge is turning into too huge to regulate and use with conventional instruments. the answer: imposing a massive info system.
As giant info Made effortless: A operating consultant to the full Hadoop Toolset exhibits, Apache Hadoop bargains a scalable, fault-tolerant method for storing and processing info 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 tremendous info many types of the reality and a few outright falsehoods born of lack of expertise. what's wanted is a publication similar to this one: a wide-ranging yet simply understood set of directions to provide an explanation for the place to get Hadoop instruments, what they could do, the way to set up them, the best way to configure them, find out how to combine them, and the way to take advantage of them effectively. and also you want a professional who has labored during this region for a decade—someone similar to writer and massive facts professional Mike Frampton.
Big information Made Easy techniques the matter of dealing with titanic info units from a platforms point of view, and it explains the jobs for every undertaking (like architect and tester, for instance) and exhibits how the Hadoop toolset can be utilized at each one process level. It explains, in an simply understood demeanour and during a number of examples, tips on how to use every one device. The e-book additionally explains the sliding scale of instruments to be had based upon info dimension and whilst and the way to exploit them. Big information Made Easy exhibits builders and designers, in addition to testers and undertaking managers, how to:
- Store substantial data
- Configure gigantic data
- Process significant data
- Schedule processes
- Move facts between SQL and NoSQL systems
- Monitor data
- Perform large facts analytics
- Report on great information tactics and projects
- Test large information systems
Big facts Made Easy additionally explains the simplest half, that's that this toolset is loose. an individual can obtain it and—with the aid of this book—start to exploit it inside of an afternoon. With the talents this ebook will educate you lower than your belt, you are going to upload worth on your corporation or shopper instantly, let alone your career.
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Additional info for Big Data Made Easy: A Working Guide to the Complete Hadoop Toolset
The ZooKeeper Client An alternative to the nc command method is to use the built-in ZooKeeper client to access your servers. You can find it with the type command, as follows: [hadoop@hc1r1m3 ~]$ type zookeeper-client zookeeper-client is /usr/bin/zookeeper-client By default, the client connects to ZooKeeper on the local server: [hadoop@hc1r1m3 ~]$ zookeeper-client Connecting to localhost:2181 36 Chapter 2 ■ Storing and Configuring Data with Hadoop, YARN, and ZooKeeper You can also get a list of possible commands by entering any unrecognized command, such as help: [zk: localhost:2181(CONNECTED) 1] help ZooKeeper -server host:port cmd args connect host:port get path [watch] ls path [watch] set path data [version] rmr path delquota [-n|-b] path quit printwatches on|off create [-s] [-e] path data acl stat path [watch] close ls2 path [watch] history listquota path setAcl path acl getAcl path sync path redo cmdno addauth scheme auth delete path [version] setquota -n|-b val path To connect to one of the other ZooKeeper servers in the quorum, you would use the connect command, specifying the server and its connection port.
You have checked the logs and found no errors. So, you are ready to attempt a test of Map Reduce. Try issuing the word-count job on the Poe data, as was done earlier for Hadoop V1 : 1. 2. 3. txt /user/hadoop/edgar/edgar 4. MapTask: Processing split: hdfs://hc1nn/user/hadoop/edgar/edgar/10947-8. mapred. MapTask: data buffer = ........ JobClient: Total committed heap usage (bytes)=1507446784 Notice that the Hadoop jar command is very similar to that used in V1. You have specified an example jar file to use, from which you will execute the word-count function.
Summary In this chapter you have been introduced to both Hadoop V1 and V2 in terms of their installation and use. It is hoped you can see that, by using the CDH stack release, the installation process and use of Hadoop are much simplified. In the course of this chapter you have installed Hadoop V1 manually via a download package from the Hadoop site. You have then installed V2 and YARN via CDH packages and the yum command. Servers for HDFS and YARN are started as Linux services in V2 rather than as scripts, as in V1.
Big Data Made Easy: A Working Guide to the Complete Hadoop Toolset by Michael Frampton