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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

Elasticsearch as a data source

In general, Elasticsearch shouldn’t (subjective, yes we do acknowledge this) be used as a primary data store. However, this question is more use case-driven and for some use cases it could very well be used as a data store. Elasticsearch does fall into the NoSQL type of database and doesn't support the ACID property of a typical relational data store, mostly used for transaction-oriented use cases. But it does have features such as optimistic locking and eventual consistency making it apt for certain pointed use cases. For a data lake implementation, it could very well act as a data store because the real data store (system of record) is with the source systems. In the case of any failure, the data could very well be warmed into Elasticsearch (in practical scenarios this is not that straight forward.. smiley) from these source system or even from our Hadoop and back to...

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