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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 sink


Similar to the source, the sink is managed by SinkRunne, which manages the thread and execution model. Unlike a source, however, a sink is polling-based and polls the channel for events. The sink is the component that outputs (according to type of output required) it from the agent to an external or other source. Sinks also participate in transaction management, and when the output from a sink is successful, an acknowledgement is passed back to the channel. The channel then takes the event away from the persistence mechanism. Transaction management will be covered in detail in a separate section.

There are a variety of existing sinks available, as follows:

  • HDFS: Write to HDFS. This currently supports writing text and sequence files (in compressed format as well). The following is a sample HDFS sink configuration (taken from Flume user guide) for an agent named a1. The full configuration can be found in the Flume user guide (https://flume.apache.org):
a1.channels = c1
a1.sinks = k1...
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