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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 storage

Storage is one of the most critical parts of a Data Lake. Apache Hadoop (HDFS) is the core of data storage for our Data Lake. The following figure sums up this aspect quite clearly, showing batch and stream data storage components in our Data Lake, along with other technologies within Hadoop Ecosystem with regards to various aspects dealing with the storage:


Figure 02: Apache Hadoop (HDFS) as data storage

We will now understand some important concepts in data storage area. We will also concentrate explicitly on batch and speed data and how these gets stored in the Data Lake and also see some specific details in regards to these data types.

Zones in Data Storage

Even though the data in the storage need not follow a certain...

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