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Mastering Hadoop 3

You're reading from  Mastering Hadoop 3

Product type Book
Published in Feb 2019
Publisher Packt
ISBN-13 9781788620444
Pages 544 pages
Edition 1st Edition
Languages
Authors (2):
Chanchal Singh Chanchal Singh
Profile icon Chanchal Singh
Manish Kumar Manish Kumar
Profile icon Manish Kumar
View More author details
Toc

Table of Contents (23) Chapters close

Title Page
Dedication
About Packt
Foreword
Contributors
Preface
1. Journey to Hadoop 3 2. Deep Dive into the Hadoop Distributed File System 3. YARN Resource Management in Hadoop 4. Internals of MapReduce 5. SQL on Hadoop 6. Real-Time Processing Engines 7. Widely Used Hadoop Ecosystem Components 8. Designing Applications in Hadoop 9. Real-Time Stream Processing in Hadoop 10. Machine Learning in Hadoop 11. Hadoop in the Cloud 12. Hadoop Cluster Profiling 13. Who Can Do What in Hadoop 14. Network and Data Security 15. Monitoring Hadoop 1. Other Books You May Enjoy Index

Stream data ingestion


Data ingestion represents a mechanism in which data is moved from a specific type of source to destination storage, where it can be further used for advanced analytics. Where there are very large data volumes, data is generally streamed to the destination storage, but only on the condition that the source and destination systems are capable of handling continuous streams of data. Stream data ingestion can be of one of two types: one is event-based and another one uses message queues.

Flume event-based data ingestion

Flume is a highly available distributed system that is used for streaming data ingestion. It collects, aggregates, and processes streaming data on the fly and stores it on disk for reliability. The following diagram shows the flume architecture:

The preceding diagram represents the architecture components of the flume components, the details of which are mentioned in the following points:

  • Flume Sources: These grasp events from external sources and pass them...
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