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Data Lake for Enterprises

You're reading from   Data Lake for Enterprises Lambda Architecture for building enterprise data systems

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Product type Paperback
Published in May 2017
Publisher Packt
ISBN-13 9781787281349
Length 596 pages
Edition 1st Edition
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Authors (3):
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Pankaj Misra Pankaj Misra
Author Profile Icon Pankaj Misra
Pankaj Misra
Tomcy John Tomcy John
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Tomcy John
Vivek Mishra Vivek Mishra
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Vivek Mishra
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Data FREE CHAPTER 2. Comprehensive Concepts of a Data Lake 3. Lambda Architecture as a Pattern for Data Lake 4. Applied Lambda for Data Lake 5. Data Acquisition of Batch Data using Apache Sqoop 6. Data Acquisition of Stream Data using Apache Flume 7. Messaging Layer using Apache Kafka 8. Data Processing using Apache Flink 9. Data Store Using Apache Hadoop 10. Indexed Data Store using Elasticsearch 11. Data Lake Components Working Together 12. Data Lake Use Case Suggestions

Flume Channel


A channel is a mechanism used by the Flume agent to transfer data from source to sink. The events are persisted in the channel and until it is delivered/taken away by a sink, they reside in the channel. This persistence in channel allows sink to retry for each event in case there is a failure while persisting data to the real store (HDFS).

Channels can be broadly categorized into two:

  1. In-memory: The events are available until the channel component is alive:
    • Queue: In-memory queues in the channel. This has the lowest latency time for processing because the events are persisted in memory.
  2. Durable: Even after the component is dead, the event persisted is available, and when the component becomes online, these events will be processed:
    • File (WAL or Write-Ahead Log): The most used channel type. It's durable and requires disk to be RAID, SAN or similar.
    • JDBC: A proper RDBMS backed channel that provides ACID compliance.
    • Kafka: stored in Kafka cluster.

There is another special channel called...

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