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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

What is Lambda Architecture?


Lambda Architecture is not technology dependent; rather it is agnostic of technology and defines some practical and well-versed principles to handle and cater to big data. It is a very generic pattern that tries to cater to common requirements raised by most big data applications. The pattern allows us to deal with both historical data and real-time data alongside each other. We used to have two different applications catering to transactional –– OnLine Transaction Processing (OLTP) and analytical –– OnLine Analytical Processing (OLAP) data, but we couldn't mix these together; rather they live separately and don't talk to each other.

These bullet points describe what a Lambda Architecture is:

  • Set of patterns and guidelines. This defines a set of patterns and guidelines for the big data kind of applications. More importantly, it allows the queries to consider both historical and newly generated data alike and gives the desired view for the analysts.
  • Deals with both...
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