Bringing Digital Transformation from a Traditional RDBMS Centric Solution to a Big Data Platform with Azure Data Lake Store

Ekta Maini (), Bondu Venkateswarlu () and Arbind Gupta
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Ekta Maini: Dayananda Sagar University
Bondu Venkateswarlu: Dayananda Sagar University
Arbind Gupta: Dayananda Sagar College of Engineering

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 595-601 from Springer

Abstract: Abstract This is an era of data explosion. The amount of data being created and stored on a global level is almost unimaginable, and it just keeps growing. Immense information can be obtained from this data by applying various techniques of analytics. It has been observed that in the present-day scenario, there are many constraints associated with data acquisition, storage, analysis, search, sharing, transfer, visualization, querying updation, privacy, security etc. Data Lakes are emerging as one of the promising possible solutions to tackle these issues. The key feature of the data lakes is that the structured, unstructured and semi structured data can be stored in their raw format. Azure Data Lake Store (ADLS) provides optimized and best solutions for a wide range of Big Data analytics. It holds its base in Hadoop distributed file system (HDFS). It is quite scalable, secure and agile. ADLS supports multiple storage tiers at exabyte scale. This paper discusses the concepts of big data and data lake in addition to the architecture of Azure data lake.

Keywords: Azure Data Lake Store (ADLS); Big data; Data Lake; Enterprise Data Warehouse (EDW) (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_58

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DOI: 10.1007/978-3-030-41862-5_58

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