Hadoop Pig was initially developed at Yahoo to allow people using Hadoop to focus more on analyzing large datasets and spend less time writing mappers and reduce programs. This would allow people to do what they want to do instead of thinking about mapper and reducer tasks. Name Pig was given to the programming language with a hint on it being designed to handle any kind of data, which has a resemblance to an actual pig, who eat almost anything.
Pig is made up of two components: the first is the language itself, which is called PigLatin, and the second is a runtime environment where PigLatin programs are executed. The program written in Pig can be split into three stages: LOAD, Transformations, and DUMP. First, you load the data you want to manipulate from HDFS. Then you run the data through a set of transformations (which subsequently are translated into a set of mapper and reducer tasks). Finally, you DUMP the data to the screen or you STORE the results in a file somewhere.
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Recommended reading list:
|Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale
Get ready to unlock the power of your data. With the fourth edition of this comprehensive guide, you’ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. This book is ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters.
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Learn two data formats: Avro for data serialization and Parquet for nested data
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|Hadoop Application Architectures: Designing Real-World Big Data Applications
Get expert guidance on architecting end-to-end data management solutions with Apache Hadoop. While many sources explain how to use various components in the Hadoop ecosystem, this practical book takes you through architectural considerations necessary to tie those components together into a complete tailored application, based on your particular use case.
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|Data Analytics with Hadoop: An Introduction for Data Scientists
Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce.
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|Hadoop: The Definitive Guide
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You’ll find illuminating case studies that demonstrate how Hadoop is used to solve specific problems. This third edition covers recent changes to Hadoop, including material on the new MapReduce API, as well as MapReduce 2 and its more flexible execution model (YARN).
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With Hadoop 2.x and YARN, Hadoop moves beyond MapReduce to become practical for virtually any type of data processing. Hadoop 2.x and the Data Lake concept represent a radical shift away from conventional approaches to data usage and storage. Hadoop 2.x installations offer unmatched scalability and breakthrough extensibility that supports new and existing Big Data analytics processing methods and models.
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Eadline concisely introduces and explains every key Hadoop 2 concept, tool, and service, illustrating each with a simple “beginning-to-end” example and identifying trustworthy, up-to-date resources for learning more.
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