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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

Product type Paperback

Published in May 2018

Publisher Packt

ISBN-13 9781788628846

Length 482 pages

Edition 1st Edition

Languages

Python

Tools

Hadoop

Concepts

Big Data

Author (1):

Sridhar Alla

View More author details

Table of Contents (13) Chapters

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

Amazon EMR

Amazon EMR is a managed cluster platform that simplifies running big data frameworks, such as Apache Hadoop and Apache Spark, on AWS to process and analyze vast amounts of data. By using these frameworks and related open source projects, such as Apache Hive and Apache Pig, you can process data for analytics purposes and business intelligence workloads. You can also use Amazon EMR to transform and move large amounts of data in and out of other AWS data stores and databases, such as Amazon S3 and Amazon DynamoDB.

Amazon EMR provides a managed Hadoop framework that is easy, fast, and cost-effective in order to process vast amounts of data across dynamically scalable Amazon EC2 instances. You can also run other popular distributed frameworks such as Apache Spark, HBase, Presto, and Flink in Amazon EMR, and interact with data in other AWS data stores such as Amazon S3 and Amazon DynamoDB.

Amazon EMR securely and reliably handles a broad set of big data use cases, including log analysis...

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Authors (1)

Sridhar Alla

Sridhar Alla

Sridhar?Alla?is the co-founder and CTO of Blue Whale Consulting and is expert at helping companies (big and small) define their vision for systems and capabilities that will allow them to establish a strategic execution plan to deal with the ever-growing data collected to support analytics and product teams. He has very experienced at dealing with all aspects of data collection, security, governance, and processing as part of end-to-end big data analytics and machine learning initiatives (including predictive modeling, deep learning, and ML automation). Sridhar?is a published book author and an avid presenter at numerous conferences, including Strata, Hadoop World, and Spark Summit.? He also has several patents filed with the US PTO on large-scale computing and distributed systems.? He has over 18 years' experience writing code in Scala, Java, C, C++, Python, R, and Go, and has extensive hands-on knowledge of Spark, Flink, TensorFlow, Keras, Hadoop, Cassandra, HBase, MongoDB, Riak, Redis, Zeppelin, Mesos, Docker, Kafka, ElasticSearch, Solr, H2O, machine learning, text analytics, distributed computing, and high-performance computing. Sridhar lives with his wife and daughter in New Jersey and in his spare time loves blogging and coaching organizations on next-generation advancements in technology and their alignment with business goals.

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