Download e-book for kindle: Beginning Apache Pig Big Data Processing Made Easy by Balaswamy Vaddeman

By Balaswamy Vaddeman

Discover ways to use Apache Pig to enhance light-weight huge info functions simply and fast. This ebook indicates you several optimization recommendations and covers each context the place Pig is utilized in significant info analytics. starting Apache Pig exhibits you ways Pig is simple to profit and calls for fairly little time to enhance gigantic facts functions. The e-book is split into 4 elements: the whole gains of Apache Pig integration with different instruments the best way to clear up advanced company difficulties and optimization of instruments. Youll notice subject matters corresponding to MapReduce and why it can't meet each company want the beneficial properties of Pig Latin corresponding to info forms for every load, shop, joins, teams, and ordering how Pig workflows may be created filing Pig jobs utilizing Hue and dealing with Oozie. Youll additionally see how you can expand the framework by way of writing UDFs and customized load, shop, and filter out capabilities. ultimately youll disguise varied optimization suggestions equivalent to accumulating facts a couple of Pig script, becoming a member of concepts, parallelism, and the position of knowledge codecs in reliable functionality. What you'll research Use the entire positive aspects of Apache Pig combine Apache Pig with different instruments expand Apache Pig Optimize Pig Latin code remedy diversified use circumstances for Pig Latin Who This booklet Is For All degrees of IT execs: architects, monstrous info fanatics, engineers, builders, and massive facts directors

Show description

Read or Download Beginning Apache Pig Big Data Processing Made Easy PDF

Best data mining books

Mark Last, Abraham Kandel, Horst Bunke's Data mining in time series databases PDF

This skinny ebook provides 8 educational papers discussing dealing with of sequences. i didn't locate any of them fascinating by itself or solid as a survey, yet lecturers doing examine in computer studying may well disagree. while you're one, you probably can get the unique papers. while you're a practitioner, move with no moment suggestion.

Download PDF by Yanchang Zhao: Post-mining of Association Rules: Techniques for Effective

There's frequently a great number of organization ideas stumbled on in facts mining perform, making it tricky for clients to spot those who are of specific curiosity to them. as a result, it is very important eliminate insignificant ideas and prune redundancy in addition to summarize, visualize, and post-mine the stumbled on principles.

Download e-book for iPad: Social Sensing: Building Reliable Systems on Unreliable Data by Dong Wang, Tarek Abdelzaher, Lance Kaplan

More and more, humans are sensors enticing without delay with the cellular net. contributors can now proportion real-time reports at an unheard of scale. Social Sensing: development trustworthy platforms on Unreliable facts appears to be like at contemporary advances within the rising box of social sensing, emphasizing the main challenge confronted via software designers: find out how to extract trustworthy info from facts accrued from mostly unknown and doubtless unreliable resources.

Get Knowledge Engineering and Semantic Web: 7th International PDF

This e-book constitutes the refereed lawsuits of the seventh foreign convention on wisdom Engineering and the Semantic net, KESW 2016, held in Prague, Czech Republic, in September 2016. The 17 revised complete papers awarded including nine brief papers have been rigorously reviewed and chosen from fifty three submissions.

Extra resources for Beginning Apache Pig Big Data Processing Made Easy

Example text

It supports indexing and ACID properties. Hive has some useful components such as the metastore, Hive Query Language, HCatalog, and Hive Server. • The metastore stores table metadata and stats in an RDBMS such as MySQL, Postgres, or Oracle. By default it stores metadata in the embedded RDBMS Apache Derby. • The Hive Query Language (HQL) is a SQL interface to Hadoop that is compiled into MapReduce code. Queries can be submitted through the command-line interface (CLI), the web interface, a Thrift client, an ODBC interface, or a JDBC interface.

Cascading Cascading extensions are available in different languages. Scalding is Scala-based, Cascalog is Clojure-based, and PyCascading is Python-based. All are programming language–based. Though it takes relatively less development time than MapReduce, it cannot be used for ad hoc querying. In Pig Latin, the programming language is required only for advanced analytics, not for simple functions. The word count program in Cascading requires 30 lines of code, and Pig requires only five lines of code.

Most of the time, you will not remember job IDs, so you can use the mapred job -list command to get currently running jobs. Grunt> kill job_201512120001_001; set The set command applies a value to a property name, for both Pig and Hadoop. It can be used in the Grunt shell and Pig Latin scripts. This section shows Pig properties for which you can set values. The set default_parallel command specifies the default number of reducers; in this example, it sets the default number of reducers to 20: Grunt>set default_parallel 20; The set debug command enables and disables debugging in a Pig Latin script.

Download PDF sample

Beginning Apache Pig Big Data Processing Made Easy by Balaswamy Vaddeman


by Michael
4.0

Rated 4.11 of 5 – based on 49 votes