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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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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

Context in data lake - data acquisition


The process of inducting data from various source systems is called data acquisition. In our data lake, we have a layer defined (in fact, the first one) which has only this responsibility to take care of.

One of the main technologies that we see doing the main job of inducting data into our data lake is using Apache Sqoop. The following sections of this chapter aim at covering Sqoop in detail so that you get a clear picture of this technology as well as get to know the data acquisition layer in detail.

Data acquisition layer

In Chapter 2, Comprehensive Concepts of a Data Lake you got a glimpse of the data acquisition layer. This layer’s responsibility is to gather information from various source systems and induct it into the data lake. This figure will refresh your memory and give you a good pictorial view of this layer:

Figure 01: Data lake - data acquisition layer

The acquisition layer should be able to handle the following:

  • Bulk data: Bulk data in the...
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