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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

When to use Flink


Select Flink as your data processing technology when:

  • You need high performance. Flink at the moment is one of the best in performance for stream processing.
  • Your use case needs machine learning. Flink’s native closed loop iterations operators make the processing perform much faster.
  • Your use case needs graph processing. Again, because of the preceding same feature, Flink will process data faster.
  • You require high throughput rates with guaranteed consistency.
  • You need exactly one time processing. This also eliminates duplicate record processing.
  • You want to avoid handling memory manually and leave that to the framework. Flink has automatic memory management.
  • You need to deal with intermediate results and Flink follows the data flow approach making it easy to do this.
  • You need less configuration. Many aspects in Flink are abstracted away from the user and this makes configuration simple.
  • You need to deal with both batch and stream data using the same framework. Flink is a hybrid...
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