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020 _a9781484262511
024 7 _a10.1007/978-1-4842-6252-8
_2doi
040 _dWaSeSS
_cAIMIT LIBRARY
050 4 _aQA76.76.M52
072 7 _aUMP
_2bicssc
072 7 _aCOM051380
_2bisacsh
072 7 _aUMP
_2thema
082 0 4 _a005.448
_21
_bCHAH
092 _aEBOOK
100 1 _aChawla, Harsh.
_932728
245 1 0 _aData lake analytics on microsoft azure
_bpractitioner's guide to big data engineering /
_cBy Harsh Chawla and Pankaj Khattar ; Foreword by Sandeep J Alur.
250 _a1st ed.
260 _aBerkeley, CA :
_bApress :
_bImprint: Apress ,
_c2020.
300 _axvii,222p. ;
_bPB
_c25.5 cm
347 _atext file
_bPDF
_2rda
505 0 _aChapter 1: Data Lake Analytics Concepts -- Chapter 2: Building Blocks of Data Analytics -- Chapter 3: Data Analytics on Public Cloud -- Chapter 4: Data Ingestion -- Chapter 5: Data Storage -- Chapter 6: Data Preparation and Training Part I -- Chapter 7: Data Preparation and Training Part II -- Chapter 8: Model and Serve -- Chapter 9: Summary.
506 _aRequires an SPL library card.
520 _aGet a 360-degree view of how the journey of data analytics solutions has evolved from monolithic data stores and enterprise data warehouses to data lakes and modern data warehouses. You will learn from the authors' experience working with large-scale enterprise customer engagements. This book includes comprehensive coverage of how: To architect data lake analytics solutions by choosing suitable technologies available on Microsoft Azure The advent of microservices applications covering ecommerce or modern solutions built on IoT and how real-time streaming data has completely disrupted this ecosystem These data analytics solutions have been transformed from solely understanding the trends from historical data to building predictions by infusing machine learning technologies into the solutions Data platform professionals who have been working on relational data stores, non-relational data stores, and big data technologies will find the content in this book useful. The book also can help you start your journey into the data engineer world as it provides an overview of advanced data analytics and touches on data science concepts and various artificial intelligence and machine learning technologies available on Microsoft Azure. You will understand the: Concepts of data lake analytics, the modern data warehouse, and advanced data analytics Architecture patterns of the modern data warehouse and advanced data analytics solutions Phases-such as Data Ingestion, Store, Prep and Train, and Model and Serve-of data analytics solutions and technology choices available on Azure under each phase In-depth coverage of real-time and batch mode data analytics solutions architecture Various managed services available on Azure such as Synapse analytics, event hubs, Stream analytics, CosmosDB, and managed Hadoop services such as Databricks and HDInsight.
538 _aMode of access: World Wide Web.
650 0 _aMicrosoft software
_932729
650 0 _aMicrosoft .NET Framework
_932730
650 0 _aBig data
_932731
655 7 _aElectronic books.
_2local
_932732
700 1 _aKhattar, Pankaj.
_932733
700 1 _aAlur, Sandeep J.
_e(Foreword by)
_932778
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9781484262511
776 0 8 _iPrinted edition:
_z9781484262535
776 0 8 _iPrinted edition:
_z9781484267240
856 4 0 _yView this electronic item in O'Reilly Online Learning: Academic/Public Library Edition.
_uhttps://ezproxy.spl.org/login?url=https://learning.oreilly.com/library/view/~/9781484262528/?ar
_zAn e-book available through full-text database.
942 _2ddc
_cBK
_e1st
_k005.448 CHAH
999 _c222706
_d222706
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