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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

AWS Glue

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it simple and cost-effective to categorize your data, clean it, enrich it, and move it reliably between various data stores. AWS Glue consists of a central data repository known as the AWS Glue Data Catalog, an ETL engine that automatically generates Python code, and a flexible scheduler that handles dependency resolution, job monitoring, and job retries/reattempts on failure. AWS Glue is serverless, so there's no infrastructure to set up or manage.

Use the AWS Glue console to discover data, transform it, and make it available for searching and querying. The console calls the underlying services to orchestrate the work required to transform your data. You can also use the AWS Glue API operations to interface with AWS Glue services. Edit, debug, and test your Python or Scala Apache Spark ETL code using a familiar development environment.

When should I use AWS Glue?

You can use AWS Glue to build a data...

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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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