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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

How Data Lake works?


In order to realize the benefits of a Data Lake, it is important to know how a Data Lake may be expected to work and what components architecturally may help to build a fully functional Data Lake. Before we pounce on the architectural details, let us understand the life cycle of data in the context of a Data Lake.

At a high level, the life cycle of a data lake may be summarized as shown here:

Figure 01: Data Lake life cycle

These can also be called various stages of data as it lives within the Data Lake. The data thus acquired can be processed and analyzed in various ways. The processing and data analysis could be a batch process or it could even be a near-real-time process. Both of these kinds of processing are expected to be supported by a Data Lake implementation as both of these patterns serve very specific use cases. The choice between the type of processing and analysis (batch/near-real-time) may also depend on the amount of processing or analysis to be performed...

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