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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

Introduction to data analytics


Data analytics is the process of applying qualitative and quantitative techniques when examining data, with the goal of providing valuable insights. Using various techniques and concepts, data analytics can provide the means to explore the data exploratory data analysis (EDA) as well as draw conclusions about the data confirmatory data analysis (CDA). The EDA and CDA are fundamental concepts of data analytics, and it is important to understand the differences between the two.

EDA involves the methodologies, tools, and techniques used to explore data with the intention of finding patterns in the data and relationships between various elements of the data. CDA involves the methodologies, tools, and techniques used to provide an insight or conclusion for a specific question, based on hypothesis and statistical techniques, or simple observation of the data.

Inside the data analytics process

Once data is deemed ready, it can be analyzed and explored by data scientists...

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