By Balaswamy Vaddeman

Discover ways to use Apache Pig to advance light-weight huge information functions simply and fast. This e-book indicates you several optimization ideas and covers each context the place Pig is utilized in substantial facts analytics. starting Apache Pig exhibits you the way Pig is straightforward to benefit and calls for really little time to improve giant information purposes. The booklet is split into 4 elements: the whole positive aspects of Apache Pig integration with different instruments easy methods to resolve complicated company difficulties and optimization of instruments. Youll realize themes corresponding to MapReduce and why it can't meet each company desire the beneficial properties of Pig Latin resembling facts 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 tips to expand the framework through writing UDFs and customized load, shop, and filter out services. eventually youll disguise diversified optimization concepts equivalent to amassing facts a few Pig script, becoming a member of thoughts, parallelism, and the position of knowledge codecs in solid functionality. What you are going to research Use all of the good points 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 ebook Is For All degrees of IT pros: architects, enormous information fanatics, engineers, builders, and massive info directors

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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. Use Cases Because Hive has a SQL interface, it was a quickly adopted Hadoop abstraction in businesses.

Apache Pig is instrumental in writing big functionality with few lines of code. 19 Chapter 1 ■ MapReduce and Its Abstractions Summary Traditional RDBMSs and data warehouse systems cannot scale to the growing size and needs of data management, so Google introduced two parallel computing technologies, GFS and MapReduce, to address big data problems related to storage and data processing. Doug Cutting, inspired by the Google technologies, created a technology called Hadoop with two modules: HDFS and MapReduce.

Apache Hive is used in data mining, R&D, ETL, machine learning, and reporting areas. Many business intelligence tools provide facilities to connect to a Hive warehouse. Some tools include Teradata, Aster data, Tableau, and Cognos. Apache Pig Pig is a platform for analyzing large data sets with a sophisticated environment for optimization and debugging. It introduced a scripting-based language called Pig Latin that is used for data processing. Pig Latin is data flow language that follows a step-bystep process to analyze data.

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