國立高雄大學圖資館 |

Language: English

Back

Data lake analytics on Microsoft Azu...

Chawla, Harsh.

Data lake analytics on Microsoft Azurea practitioner's guide to big data engineering /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data lake analytics on Microsoft Azureby Harsh Chawla, Pankaj Khattar.
Reminder of title:	a practitioner's guide to big data engineering /
Author:	Chawla, Harsh.
other author:	Khattar, Pankaj.
Published:	Berkeley, CA :Apress :2020.
Description:	xvii, 222 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Microsoft Azure (Computing platform)
Online resource:	https://doi.org/10.1007/978-1-4842-6252-8
ISBN:	9781484262528$q(electronic bk.)

Data lake analytics on Microsoft Azurea practitioner's guide to big data engineering /
Chawla, Harsh.

Data lake analytics on Microsoft Azurea practitioner's guide to big data engineering /[electronic resource] :by Harsh Chawla, Pankaj Khattar. - Berkeley, CA :Apress :2020. - xvii, 222 p. :ill., digital ;24 cm.

Chapter 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.

Get 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.

ISBN: 9781484262528$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-6252-8doiSubjects--Uniform Titles:

Microsoft .NET Framework.
Subjects--Topical Terms:

763318
Microsoft Azure (Computing platform)

LC Class. No.: QA76.9.B45

Dewey Class. No.: 004.165

Data lake analytics on Microsoft Azurea practitioner's guide to big data engineering /
LDR:03248nmm a2200325 a 4500 001 589076
003 DE-He213
005 20210205095208.0
006 m d
007 cr nn 008maaau
008 210525s2020 cau s 0 eng d
020 $a 9781484262528$q(electronic bk.)
020 $a 9781484262511$q(paper)
024 7 $a 10.1007/978-1-4842-6252-8 $2 doi
035 $a 978-1-4842-6252-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.B45
072 7 $a UMP $2 bicssc
072 7 $a COM051380 $2 bisacsh
072 7 $a UMP $2 thema
082 0 4 $a 004.165 $2 23
090 $a QA76.9.B45 $b C512 2020
100 1 $a Chawla, Harsh. $3 849909
245 1 0 $a Data lake analytics on Microsoft Azure $h [electronic resource] : $b a practitioner's guide to big data engineering / $c by Harsh Chawla, Pankaj Khattar.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2020.
300 $a xvii, 222 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 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.
520 $a Get 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.
630 0 0 $a Microsoft .NET Framework. $3 797678
650 0 $a Microsoft Azure (Computing platform) $3 763318
650 0 $a Big data. $3 609582
650 1 4 $a Microsoft and .NET. $3 760507
650 2 4 $a Big Data. $3 760530
700 1 $a Khattar, Pankaj. $3 880787
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-6252-8
950 $a Professional and Applied Computing (SpringerNature-12059)