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Big Data Analytics with Hadoop 3

You're reading from   Big Data Analytics with Hadoop 3 Build highly effective analytics solutions to gain valuable insight into your big data

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788628846
Length 482 pages
Edition 1st Edition
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Author (1):
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Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Hadoop FREE CHAPTER 2. Overview of Big Data Analytics 3. Big Data Processing with MapReduce 4. Scientific Computing and Big Data Analysis with Python and Hadoop 5. Statistical Big Data Computing with R and Hadoop 6. Batch Analytics with Apache Spark 7. Real-Time Analytics with Apache Spark 8. Batch Analytics with Apache Flink 9. Stream Processing with Apache Flink 10. Visualizing Big Data 11. Introduction to Cloud Computing 12. Using Amazon Web Services

Apache Spark

Apache Spark is a unified distributed computing engine across different workloads and platforms. Spark can connect to different platforms and process different data workloads using a variety of paradigms such as Spark Streaming, Spark ML, Spark SQL, and Spark Graphx.

Apache Spark is a fast in-memory data processing engine with elegant and expressive development APIs, which allow data workers to efficiently execute streaming machine learning or SQL workloads that require fast interactive access to data sets. 

Additional libraries built on top of the core allow the workloads for streaming, SQL, graph processing, and machine learning. SparkML, for instance, is designed for data science and its abstraction makes data science easier.

Spark provides real-time streaming, queries, machine learning, and graph processing. Before Apache Spark, we had to use different technologies...

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