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Hadoop Essentials

You're reading from  Hadoop Essentials

Product type Book
Published in Apr 2015
Publisher Packt
ISBN-13 9781784396688
Pages 194 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Shiva Achari Shiva Achari
Profile icon Shiva Achari
LinkedIn
Shiva Achari has over 8 years of extensive industry experience and is currently working as a Big Data Architect consultant with companies such as Oracle and Teradata. Over the years, he has architected, designed, and developed multiple innovative and high-performance large-scale solutions, such as distributed systems, data centers, big data management tools, SaaS cloud applications, Internet applications, and Data Analytics solutions. He is also experienced in designing big data and analytics applications, such as ingestion, cleansing, transformation, correlation of different sources, data mining, and user experience in Hadoop, Cassandra, Solr, Storm, R, and Tableau. He specializes in developing solutions for the big data domain and possesses sound hands-on experience on projects migrating to the Hadoop world, new developments, product consulting, and POC. He also has hands-on expertise in technologies such as Hadoop, Yarn, Sqoop, Hive, Pig, Flume, Solr, Lucene, Elasticsearch, Zookeeper, Storm, Redis, Cassandra, HBase, MongoDB, Talend, R, Mahout, Tableau, Java, and J2EE. He has been involved in reviewing Mastering Hadoop, Packt Publishing. Shiva has expertise in requirement analysis, estimations, technology evaluation, and system architecture along with domain experience in telecoms, Internet applications, document management, healthcare, and media. Currently, he is supporting presales activities such as writing technical proposals (RFP), providing technical consultation to customers, and managing deliveries of big data practice groups in Teradata.
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Table of Contents (15) Chapters

Hadoop Essentials
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
1. Introduction to Big Data and Hadoop 2. Hadoop Ecosystem 3. Pillars of Hadoop – HDFS, MapReduce, and YARN 4. Data Access Components – Hive and Pig 5. Storage Component – HBase 6. Data Ingestion in Hadoop – Sqoop and Flume 7. Streaming and Real-time Analysis – Storm and Spark Index

Hadoop integration

Hadoop architecture is designed to be easily integrated with other systems. Integration is very important because although we can process the data efficiently in Hadoop, but we should also be able to send that result to another system to move the data to another level. Data has to be integrated with other systems to achieve interoperability and flexibility.

The following figure depicts the Hadoop system integrated with different systems and with some implemented tools for reference:

Hadoop Integration with other systems

Systems that are usually integrated with Hadoop are:

  • Data Integration tools such as, Sqoop, Flume, and others

  • NoSQL tools such as, Cassandra, MongoDB, Couchbase, and others

  • ETL tools such as, Pentaho, Informatica, Talend, and others

  • Visualization tools such as, Tableau, Sas, R, and others

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Authors (1)

Profile icon Shiva Achari
LinkedIn
Shiva Achari has over 8 years of extensive industry experience and is currently working as a Big Data Architect consultant with companies such as Oracle and Teradata. Over the years, he has architected, designed, and developed multiple innovative and high-performance large-scale solutions, such as distributed systems, data centers, big data management tools, SaaS cloud applications, Internet applications, and Data Analytics solutions. He is also experienced in designing big data and analytics applications, such as ingestion, cleansing, transformation, correlation of different sources, data mining, and user experience in Hadoop, Cassandra, Solr, Storm, R, and Tableau. He specializes in developing solutions for the big data domain and possesses sound hands-on experience on projects migrating to the Hadoop world, new developments, product consulting, and POC. He also has hands-on expertise in technologies such as Hadoop, Yarn, Sqoop, Hive, Pig, Flume, Solr, Lucene, Elasticsearch, Zookeeper, Storm, Redis, Cassandra, HBase, MongoDB, Talend, R, Mahout, Tableau, Java, and J2EE. He has been involved in reviewing Mastering Hadoop, Packt Publishing. Shiva has expertise in requirement analysis, estimations, technology evaluation, and system architecture along with domain experience in telecoms, Internet applications, document management, healthcare, and media. Currently, he is supporting presales activities such as writing technical proposals (RFP), providing technical consultation to customers, and managing deliveries of big data practice groups in Teradata.
Read more
See other products by Shiva Achari

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