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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

Thoughts on data traceability

Traceability is the ability to verify the history, location, or application of an item by means of documented recorded identification.
- Wikipedia

Data traceability means the path followed by data in moving from one location (origin) to another (destination), various processes and transformation it undergoes while doing so before reaching its intended destination. We have already seen what data lineage is, so what is the difference between lineage and traceability?

Data lineage is often associated with metadata management and governance and has a difference to what data traceability means.

Data lineage is more technical in nature and shows each and every important step the data undergoes when going from origin to destination. This is a very important capability/resource for a technical team but doesn't give much sense to a non-technical business or other users in the enterprise...

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