By Balaswamy Vaddeman
Learn how to use Apache Pig to enhance light-weight substantial information functions simply and fast. This ebook indicates you several optimization concepts and covers each context the place Pig is utilized in vast facts analytics. starting Apache Pig indicates you ways Pig is straightforward to benefit and calls for fairly little time to improve significant info purposes. The booklet is split into 4 elements: the entire good points of Apache Pig integration with different instruments how one can resolve advanced enterprise difficulties and optimization of instruments. Youll observe themes similar to MapReduce and why it can't meet each enterprise want the beneficial properties of Pig Latin corresponding to facts kinds for every load, shop, joins, teams, and ordering how Pig workflows will 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. eventually youll disguise various optimization options comparable to accumulating records a few Pig script, becoming a member of ideas, parallelism, and the function of knowledge codecs in solid functionality. What you'll examine Use all of the good points of Apache Pig combine Apache Pig with different instruments expand Apache Pig Optimize Pig Latin code remedy varied use situations for Pig Latin Who This ebook Is For All degrees of IT execs: architects, gigantic info fanatics, engineers, builders, and massive info directors
Read or Download Beginning Apache Pig Big Data Processing Made Easy PDF
Similar data mining books
Info Mining, the automated extraction of implicit and probably valuable info from info, is more and more utilized in advertisement, clinical and different software areas.
Principles of information Mining explains and explores the critical concepts of knowledge Mining: for type, organization rule mining and clustering. each one subject is obviously defined and illustrated via unique labored examples, with a spotlight on algorithms instead of mathematical formalism. it's written for readers with out a robust historical past in arithmetic or information, and any formulae used are defined in detail.
This moment variation has been improved to incorporate extra chapters on utilizing widespread development timber for organization Rule Mining, evaluating classifiers, ensemble type and working with very huge volumes of data.
Principles of information Mining goals to assist basic readers boost the mandatory knowing of what's contained in the 'black box' to allow them to use advertisement info mining programs discriminatingly, in addition to permitting complicated readers or educational researchers to appreciate or give a contribution to destiny technical advances within the field.
Suitable as a textbook to aid classes at undergraduate or postgraduate degrees in a variety of matters together with laptop technology, company reviews, advertising, man made Intelligence, Bioinformatics and Forensic technological know-how.
Steve Lohr, a know-how reporter for the hot York instances, chronicles the increase of huge info, addressing state-of-the-art enterprise innovations and interpreting the darkish facet of a data-driven international. Coal, iron ore, and oil have been the main efficient resources that fueled the economic Revolution. this day, info is the very important uncooked fabric of the data financial system.
Additional resources for Beginning Apache Pig Big Data Processing Made Easy
Apache Hive Apache Hive was written only for a warehouse use case that can process only structured data that is available within tables. And it is declarative language that talks about what to achieve rather than how to achieve. Complex business applications require several lines of code that might have several subqueries inside. These queries are difficult to understand and are difficult to troubleshoot in case of issues. Query execution in Hive is from the innermost query to the outermost query.
Commands Two commands in Pig Latin—namely, fs and sh—help you interact with file systems and run shell scripts. The fs Command The fs command is used to invoke the fsshell commands of HDFS. This command can be executed in the Grunt shell and Pig Latin scripts. Here are a few examples of the sh command to check the input file path in the Grunt shell before specifying it as an input path and to check the output directory once the job is completed. Without the sh command, you would have to exit Grunt to check the input file path and return to Grunt to continue.
REPLACE will replace the output data if it already exists in the output directory specified. Each operator is allowed to perform an operation on each line. Here you are using the RegexSplitGenerator function that splits every line of text into words using a comma (,) as the delimiter. You are defining this pipe as words. The GroupBy class works on the words pipe to arrange words into groups and creates a new pipe called group. Later you will create a new pipe account that will apply the count operation on every group using the Every operator.
Beginning Apache Pig Big Data Processing Made Easy by Balaswamy Vaddeman