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DynamoDB Applied Design Patterns

You're reading from  DynamoDB Applied Design Patterns

Product type Book
Published in Sep 2014
Publisher
ISBN-13 9781783551897
Pages 202 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Uchit Hamendra Vyas Uchit Hamendra Vyas
Profile icon Uchit Hamendra Vyas
LinkedIn
See other products by Uchit Hamendra Vyas

Table of Contents (17) Chapters

DynamoDB Applied Design Patterns
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Data Modeling with DynamoDB 2. DynamoDB Interfaces 3. Tools and Libraries of AWS DynamoDB 4. Working with Secondary Indexes 5. Query and Scan Operations in DynamoDB 6. Working with the DynamoDB API 7. Distributed Locking with DynamoDB 8. DynamoDB with Redshift, Data Pipeline, and MapReduce 9. DynamoDB – Best Practices Comparing DynamoDB Index

Exporting, importing, querying, and joining tables using AWS MapReduce

In this section, you will learn how to use Amazon Elastic MapReduce (Amazon EMR) with a tailored version of Apache Hive that includes connectivity to DynamoDB to execute operations on data stored in DynamoDB. You can find more information about Apache Hive at https://hive.apache.org/. You will perform actions such as the following:

  • Loading DynamoDB data tables into the Hadoop Distributed File System (HDFS)

  • Querying live DynamoDB data

  • Joining data stored in DynamoDB and exporting or querying it

With Amazon EMR and Hive, you can quickly and efficiently process large amounts of data, such as the data stored in DynamoDB. Apache Hive is a layer that you can use to query a MapReduce cluster using an easy, SQL-like query language called HiveQL. It runs on the Hadoop architecture. To start with the earlier operations, you will have to launch the EMR cluster, specify the location of the DynamoDB tables, and provide the Hive command...

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Authors (1)

Profile icon Uchit Hamendra Vyas
LinkedIn
See other products by Uchit Hamendra Vyas

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