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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

Enterprise Data Management

Ability of an organization to precisely define, easily integrate and effectively retrieve data for both internal applications and external communication

- Wikipedia

EDM emphasizes data precision, granularity and meaning and is concerned with how the content is integrated into business applications as well as how it is passed along from one business process to another.

- Wikipedia

As the preceding wikipedia definition clearly states, EDM is the process or strategy of determining how this enterprise data needs to be stored, where it has to be stored, and what technologies it has to use to store and retrieve this data in an enterprise. Being very valuable, this data has to be secured using the right controls and needs to be managed and owned in a defined fashion. It also defines how the data can be taken out to communicate with both internal and external applications alike. Furthermore, the policies and processes around the data exchange have to be well defined.

Looking at the previous paragraph, it seems that it is very easy to have EDM in place for an enterprise, but in reality, it is very difficult. In an enterprise, there are multiple departments, and each department churns out data; based on the significance of these departments, the data churned would also be very relevant to the organization as a whole. Because of the distinction and data relevance, the owner of each data in EDM has different interests, causing conflicts and thus creating problems in the enterprise. This calls for various policies and procedures along with ownership of each data in EDM.

In the context of this book, learning about enterprise data, enterprise data management, and issues around maintaining an EDM are quite significant. This is the reason why it's good to know these aspects at the start of the book itself. In the following sections we will discuss big data concepts and ways in which big data can be incorporated into enterprise data management and extend its capabilities with opportunities that could not be imagined without big data technologies.

You have been reading a chapter from
Data Lake for Enterprises
Published in: May 2017
Publisher: Packt
ISBN-13: 9781787281349
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