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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

Thoughts on data security

One of the very important capabilities required for a Data Lake implementation in an enterprise is security. In a Data Lake we are bringing in data from around the enterprise into one place. You have convinced all the departments who has agreed to ingest data into the Data Lake that the data in the lake is secured and only authenticated and authorized users have access to the data. So, this aspect needs some serious thought so that data is secured and these departments are quite happy with the access rules for their all important data. In addition to security setup, proper governance through adequate processes also should be setup to make security quite sturdy but quite easy for users having access to it to do their deep analysis work.

By data security, it refers to in-flight transaction data (stream), date at rest (batch), both authentication and authorization (attributes).

Data lake does...

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