By Pradeep Pasupuleti
Pig layout Patterns is a accomplished advisor that might allow readers to with ease use layout styles that simplify the construction of advanced information pipelines in a variety of levels of information administration. This publication specializes in utilizing Pig in an company context, bridging the space among theoretical realizing and sensible implementation. each one bankruptcy includes a set of layout styles that pose after which remedy technical demanding situations which are appropriate to the firm use cases.
The publication covers the adventure of massive info from the time it enters the firm to its eventual use in analytics, within the type of a document or a predictive version. through the top of the ebook, readers will get pleasure from Pig's actual energy in addressing each challenge encountered whilst growing an analytics-based info product. each one layout trend comes with a instructed answer, interpreting the trade-offs of imposing the answer differently, explaining how the code works, and the results.
Who this publication is for
The skilled developer who's already acquainted with Pig and is seeking a use case perspective the place they could relate to the issues of knowledge ingestion, profiling, detoxification, remodeling, and egressing info encountered within the agencies. wisdom of Hadoop and Pig is critical for readers to understand the intricacies of Pig layout styles better.
About this book
• quick know how to exploit Pig to layout end-to-end large info systems
• enforce a hands-on programming method utilizing layout styles to resolve more often than not taking place firm monstrous info challenges
• complements clients’ functions to make use of Pig and create their very own layout styles anyplace acceptable
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Additional info for Pig design patterns: simplify Hadoop programming to create complex end-to-end enterprise big data solutions with Pig
Some of this data comes through well-defined processes; on the other hand though, a large majority of it comes through numerous unstructured forms, and as a result, ends up as unstructured data. Analytics tried to keep pace and mostly succeeded. However, the diversity of both the data and the desired analytics demands newer and smarter methods for working with the data. The Pig platform surely is one of these methods. Nevertheless, the power of such a platform is best tapped by extending it efficiently.
Their effort reinforces my faith in teamwork—the key ingredient for the success of any endeavor. Srinivas Uppuluri has been an inspiration right from the beginning of my career, and I am extremely proud to be associated with him. I would like to profusely thank him for reviewing this book at every step and allowing me to be exposed to many great ideas, points of view, and zealous inspiration. I would also like to thank Dr. Dakshina Murthy who eased me into the world of Big Data analytics and is my mentor and role model in the field of data sciences.
Traditional systems, such as RDBMS-based data warehouses, took the lead to support the decision-making process by being able to collect, store, and manage data by applying traditional and statistical methods of measurement to create a reporting and analysis platform. The data collected within these traditional systems were highly structured in nature with minimal flexibility to change with the needs of the emerging data types, which were more unstructured. These data warehouses are capable of supporting distributed processing applications, but with many limitations.