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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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Toc

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

Producer and consumer reliability


In distributed systems, components fail. Its a common practice to design your code to take care of these failures in a seamless fashion (fault-tolerant).

One of the ways by which Kafka tolerates failure is by maintaining the replication of messages. Messages are replicated in so called partitions and Kafka automatically elects one partition as leader and other follower partitions just replicate the leader. The leader also maintains a list of replicas which are in sync so as to make sure that ideal replication is maintained to handle failures.

The producer sends message to the topic (Kafka broker in Kafka cluster) and durability can be configured using the producer configuration, request.required.acks, which has the following values:

  • 0: message written to network/buffer
  • 1: message written directly to partition leader
  • all: producer gets an acknowledgement when all in-sync replicas (ISR’s) get the message

Consumer reads data from topics and in Kafka the state of...

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