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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

Knowing more about Data processing

Data processing is one of the important capabilities in a Data Lake implementation. Our Data Lake is no exception and does participate in data processing, both in batch and speed layer. In this section we will cover some important topics that needs to be looked upon with respect to Data Lake dealing with data processing. With Hadoop 1.x, MapReduce was one of the main processing done in Hadoop. With Hadoop 2.x and with more data ingestion methodologies, more options in the real time/streaming area have also come in and these two aspects with some important considerations are detailed here.

Data validation and cleansing

Validating data before it gets into the persistence layer of Data Lake is a very important step. Validation in the...

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