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Modern Big Data Processing with Hadoop

You're reading from  Modern Big Data Processing with Hadoop

Product type Book
Published in Mar 2018
Publisher Packt
ISBN-13 9781787122765
Pages 394 pages
Edition 1st Edition
Languages
Concepts
Authors (3):
V Naresh Kumar V Naresh Kumar
Profile icon V Naresh Kumar
Manoj R Patil Manoj R Patil
Profile icon Manoj R Patil
Prashant Shindgikar Prashant Shindgikar
Profile icon Prashant Shindgikar
View More author details
Toc

Table of Contents (12) Chapters close

Preface 1. Enterprise Data Architecture Principles 2. Hadoop Life Cycle Management 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

Summary

In this chapter, we have learned all the popular data ingestion tools used in production environments. Sqoop is mainly used to import and export data in and out of RDBMS data stores. Apache Flume is used in real-time systems to import data, mainly from files sources. It supports a wide variety of sources and sinks. Apache NiFi is a fairly new tool and getting very popular these days. It also supports GUI-based ETL development. Hortonworks has started supporting this tool since their HDP 2.4 release. Apache Kafka Connect is another popular tool in the market. It is also a part of the Confluent Data Platform. Kafka Connect can ingest entire databases or collect metrics from all your application servers into Kafka topics, making the data available for stream processing with low latency.

Since we so far know how to build Hadoop clusters and how to ingest data in them, we will...

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