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Modern Big Data Processing with Hadoop

You're reading from   Modern Big Data Processing with Hadoop Expert techniques for architecting end-to-end big data solutions to get valuable insights

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781787122765
Length 394 pages
Edition 1st Edition
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Authors (3):
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Manoj R Patil Manoj R Patil
Author Profile Icon Manoj R Patil
Manoj R Patil
Prashant Shindgikar Prashant Shindgikar
Author Profile Icon Prashant Shindgikar
Prashant Shindgikar
V Naresh Kumar V Naresh Kumar
Author Profile Icon V Naresh Kumar
V Naresh Kumar
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Toc

Table of Contents (12) Chapters Close

Preface 1. Enterprise Data Architecture Principles 2. Hadoop Life Cycle Management FREE CHAPTER 3. Hadoop Design Consideration 4. Data Movement Techniques 5. Data Modeling in Hadoop 6. Designing Real-Time Streaming Data Pipelines 7. Large-Scale Data Processing Frameworks 8. Building Enterprise Search Platform 9. Designing Data Visualization Solutions 10. Developing Applications Using the Cloud 11. Production Hadoop Cluster Deployment

Apache Hive

Hive is a data processing tool in Hadoop. As we have learned in the previous chapter, data ingestion tools load data and generate HDFS files in Hadoop; we need to query that data based on our business requirements. We can access the data using MapReduce programming. But data access with MapReduce is extremely slow. To access a few lines of HDFS files, we have to write separate mapper, reducer, and driver code. So, in order to avoid this complexity, Apache introduced Hive. Hive supports an SQL-like interface that helps access the same lines of HDFS files using SQL commands. Hive was initially developed by Facebook but was later taken over by Apache.

Apache Hive and RDBMS

I mentioned that Hive provides an SQL-like...

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