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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
Author Profile Icon Vivek Mishra
Vivek Mishra
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Toc

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

Expectations from Data Lake

Data lake does cost money to build and manage. So, the expectation from various parties from Data Lake is quite demanding and varied in nature. Let's divide these expectation into two based on parties involved.

Expectation from business users:

  • Analysis is always running on right data with good quality attributes.
  • Capability to easily manage data governance.
  • Setup security measures whereby the data visibility can be controlled in more fine grained fashion. Easy data masking capability, when needed by employing appropriate transformations controlled by authorizations mechanisms.
  • Self service capability with minimal technical knowledge for a broad spectrum of people.
  • More easy representation of data lineage and traceability
  • Should be able to support metadata management
Data lineage is defined as a data life cycle that includes the data's origins and where it moves over time....
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