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Programming MapReduce with Scalding

You're reading from   Programming MapReduce with Scalding A practical guide to designing, testing, and implementing complex MapReduce applications in Scala

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Product type Paperback
Published in Jun 2014
Publisher
ISBN-13 9781783287017
Length 148 pages
Edition 1st Edition
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Author (1):
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Antonios Chalkiopoulos Antonios Chalkiopoulos
Author Profile Icon Antonios Chalkiopoulos
Antonios Chalkiopoulos
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Table of Contents (11) Chapters Close

Preface 1. Introduction to MapReduce FREE CHAPTER 2. Get Ready for Scalding 3. Scalding by Example 4. Intermediate Examples 5. Scalding Design Patterns 6. Testing and TDD 7. Running Scalding in Production 8. Using External Data Stores 9. Matrix Calculations and Machine Learning Index

Interacting with external systems


Scalding allows us to build rich pipelines that read data from one or more sources, perform data transformations, and store results into one or more sinks. The sources and the sinks are called taps.

With Scalding, we can tap into the HDFS filesystem. A characteristic of HDFS is that it does not allow appending to files. Once a file is closed, it is immutable and can only be changed by writing a new copy with a different filename. This style of file access fits nicely with MapReduce and batch processing jobs.

There are, however, use cases where data changes very frequently, or fast response times are required for real-time applications. The use cases fit nicely with in-memory systems. Fortunately, Scalding can tap to multiple external data stores, and thus, elaborate pipelines can be achieved:

Scalding supports interaction with SQL, NoSQL, and in-memory systems either through external libraries or by using wrappers over Cascading libraries. Moreover, with Scalding...

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