By Balaswamy Vaddeman
Learn how to use Apache Pig to enhance light-weight large info functions simply and fast. This ebook exhibits you several optimization thoughts and covers each context the place Pig is utilized in titanic information analytics. starting Apache Pig exhibits you the way Pig is straightforward to benefit and calls for particularly little time to advance mammoth facts functions. The publication is split into 4 elements: the total good points of Apache Pig integration with different instruments find out how to clear up complicated enterprise difficulties and optimization of instruments. Youll detect themes resembling MapReduce and why it can't meet each enterprise desire the positive aspects of Pig Latin corresponding to information 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 the right way to expand the framework via writing UDFs and customized load, shop, and filter out capabilities. ultimately youll conceal diverse optimization innovations reminiscent of collecting records a couple of Pig script, becoming a member of techniques, parallelism, and the position of knowledge codecs in sturdy functionality. What you'll examine Use all of the gains of Apache Pig combine Apache Pig with different instruments expand Apache Pig Optimize Pig Latin code remedy varied use circumstances for Pig Latin Who This ebook Is For All degrees of IT execs: architects, monstrous info fans, engineers, builders, and massive info directors
Read or Download Beginning Apache Pig Big Data Processing Made Easy PDF
Best data mining books
Attempt to think a railway community that didn't money its rolling inventory, tune, and signs every time a failure happened, or simply came upon the whereabouts of its lo comotives and carriages in the course of annual inventory taking. simply think a railway that stored its trains ready simply because there have been no to be had locomotives.
Significant facts of complicated Networks offers and explains the tools from the research of huge information that may be utilized in analysing great structural information units, together with either very huge networks and units of graphs. in addition to utilising statistical research thoughts like sampling and bootstrapping in an interdisciplinary demeanour to supply novel thoughts for interpreting immense quantities of information, this ebook additionally explores the probabilities provided via the specific points comparable to machine reminiscence in investigating huge units of complicated networks.
This publication constitutes the refereed lawsuits of the tenth Metadata and Semantics examine convention, MTSR 2016, held in Göttingen, Germany, in November 2016. The 26 complete papers and six brief papers awarded have been rigorously reviewed and chosen from sixty seven submissions. The papers are geared up in different classes and tracks: electronic Libraries, details Retrieval, associated and Social information, Metadata and Semantics for Open Repositories, study info platforms and knowledge Infrastructures, Metadata and Semantics for Agriculture, nutrients and atmosphere, Metadata and Semantics for Cultural Collections and functions, eu and nationwide tasks.
This is often the 1st textbook on characteristic exploration, its conception, its algorithms forapplications, and a few of its many attainable generalizations. characteristic explorationis worthwhile for buying dependent wisdom via an interactive method, byasking queries to a professional. Generalizations that deal with incomplete, defective, orimprecise facts are mentioned, however the concentration lies on wisdom extraction from areliable info resource.
- Data-ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else
- Handbook of Research on Digital Libraries: Design, Development, and Impact
- Pattern Discovery Using Sequence Data Mining: Applications and Studies
- Artificial Intelligence in Medicine: 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015. Proceedings
- Machine Learning and Data Mining in Pattern Recognition: 11th International Conference, MLDM 2015, Hamburg, Germany, July 20-21, 2015, Proceedings
Extra info for Beginning Apache Pig Big Data Processing Made Easy
Table 2-3. Possible Castings Between Different Data Types From To int long float double chararray bytearray boolean int NA Yes Yes Yes Yes No No long Yes NA Yes Yes Yes No No float Yes Yes NA Yes Yes NO No double Yes Yes Yes NA Yes No No chararray Yes Yes Yes Yes NA No Yes bytearray Yes Yes Yes Yes Yes NA Yes boolean No No No No Yes No NA Comparison Operators The operators in Table 2-4 are used in Pig Latin to perform comparison operations such as equal, not equal, greater than, and so on. 29 Chapter 2 ■ Data Types Table 2-4.
You can create a new table from this table by prepending the create table as select statement like below. Create table wordcount as Benefits Hive is a scalable data warehousing system. Building a Hive team is easy because of its SQL interface. Unlike MapReduce, it is suitable for ad hoc querying. With many BI tools available on top of Hive, people without much programming experience can get insights from big data. It can easily be extensible using user-defined functions (UDFs). You can easily optimize code and also support several data formats such as text, sequence, RC, and ORC.
14 Chapter 1 ■ MapReduce and Its Abstractions LocalFlowConnector will help you to create a local flow that can be run on the local file system. You can use HadoopFlowConnector for creating a flow that works on the Hadoop file system. complete() will start executing the flow. 1. Modify the previous Cascading program to filter the word pear. Benefits These are the benefits of Cascading: • Like MapReduce, it can process all types of data, such as structured, semistructured, and unstructured data.