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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

Sqoop support for HDFS


Sqoop is natively built for HDFS export and import; however, architecturally it can support and source and target data stores for data exports and imports. In fact, if we observe the convention of the words Import and Export it is all with respect to whether the data is coming into HDFS or going out of HDFS respectively. Sqoop also supports incremental data exports and imports with having an additional attribute/fields for tracking the database incrementals.

Sqoop also supports a number of file formats for optimized storage such as Apache Avro, orc, parquet, and so on. Both parquet and Avro have been very popular file formats with respect to HDFS while orc offers better performance and compression. But as a tradeoff, parquet and Avro formats are relatively more preferred formats due to maintainability and recent enhancements for these formats in HDFS, supporting multi-value fields and search patterns.

Avro is a remote procedure call and data serialization framework developed...

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