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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

Context in Data Lake: data storage and lambda speed layer

The Data Storage Layer is where data is persisted. Our core persistent store is HDFS but because of its inherent slowness in querying, we need to have a technology on top of it for fast reading/querying. After indexing, the appropriate speed views are created and kept in the Lambda Speed Layer. The Lambda Speed Layer is entrusted with indexing the data stored in HDFS for high performance and scalable querying of required data.

Elasticsearch is our choice of technology capable of doing this effectively. Elasticsearch is one of the de-facto choices for such a capability, and with not much deliberation this technology choice was made.

The following sections of this chapter aim at covering Elasticsearch in detail so that you get a clear picture of this technology as well as get to know the data storage layer in detail.

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