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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
Author Profile Icon Tomcy John
Tomcy John
Vivek Mishra Vivek Mishra
Author Profile Icon Vivek Mishra
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 transaction management


Throughout the previous sections we have indeed transaction aspects at various stages. The following figure summarizes these discussions in a more pictorial fashion:

Figure 18: Transaction management in Flume (Source Tx and Sink Tx)

This figure shows that incoming data from a client or previous sink starts the present agent transaction and this is termed as Source Tx in the figure. The Source Tx ends soon after the event is persisted in the channel and acknowledgement received.

In purview of an agent a second transaction kicks in termed as Sink Tx which start with the data being polled by the sink and when the data is successfully transferred, channel uses the acknowledgement to remove the data in the channel.

Flume does have transaction management in all aspects and according to use case various reliability levels can be set in channel which decides how the transaction behaviour (Sink Tx) is realized.

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