國立虎尾科技大學 |

Introducing .NET for Apache Spark = Distributed Processing for Massive Datasets /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Introducing .NET for Apache Spark/ by Ed Elliott.
其他題名:	Distributed Processing for Massive Datasets /
作者:	Elliott, Ed.
面頁冊數:	XV, 262 p. 41 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Microsoft software. -
電子資源:	https://doi.org/10.1007/978-1-4842-6992-3
ISBN:	9781484269923

Introducing .NET for Apache Spark = Distributed Processing for Massive Datasets /
Elliott, Ed.

Introducing .NET for Apache SparkDistributed Processing for Massive Datasets /[electronic resource] :by Ed Elliott. - 1st ed. 2021. - XV, 262 p. 41 illus.online resource.

Part I. Getting Started -- 1. Understanding Apache Spark -- 2. Setting up Spark -- 3 -- Programming with .NET for Apache Spark -- Part II. The APIs -- 4. User-Defined Functions -- 5. The DataFrame API -- 6. Spark SQL and Hive Tables -- 7. Spark Machine Learning API -- Part III. Examples -- 8. Batch Mode Processing -- 9. Structured Streaming -- 10. Troubleshooting -- 11. Delta Lake -- Part IV. Appendices -- Appendix A. Running in the Cloud -- Appendix B. Implementing .NET for Apache Spark Code.

Get started using Apache Spark via C# or F# and the .NET for Apache Spark bindings. This book is an introduction to both Apache Spark and the .NET bindings. Readers new to Apache Spark will get up to speed quickly using Spark for data processing tasks performed against large and very large datasets. You will learn how to combine your knowledge of .NET with Apache Spark to bring massive computing power to bear by distributed processing of extremely large datasets across multiple servers. This book covers how to get a local instance of Apache Spark running on your developer machine and shows you how to create your first .NET program that uses the Microsoft .NET bindings for Apache Spark. Techniques shown in the book allow you to use Apache Spark to distribute your data processing tasks over multiple compute nodes. You will learn to process data using both batch mode and streaming mode so you can make the right choice depending on whether you are processing an existing dataset or are working against new records in micro-batches as they arrive. The goal of the book is leave you comfortable in bringing the power of Apache Spark to your favorite .NET language. You will: Install and configure Spark .NET on Windows, Linux, and macOS Write Apache Spark programs in C# and F# using the .NET bindings Access and invoke the Apache Spark APIs from .NET with the same high performance as Python, Scala, and R Encapsulate functionality in user-defined functions Transform and aggregate large datasets Execute SQL queries against files through Apache Hive Distribute processing of large datasets across multiple servers Create your own batch, streaming, and machine learning programs.

ISBN: 9781484269923

Standard No.: 10.1007/978-1-4842-6992-3doiSubjects--Topical Terms:

1253736
Microsoft software.

LC Class. No.: QA76.76.M52

Dewey Class. No.: 004.165

Introducing .NET for Apache Spark = Distributed Processing for Massive Datasets /
LDR:03500nam a22003855i 4500 001 1051156
003 DE-He213
005 20210622181326.0
007 cr nn 008mamaa
008 220103s2021 xxu| s |||| 0|eng d
020 $a 9781484269923 $9 978-1-4842-6992-3
024 7 $a 10.1007/978-1-4842-6992-3 $2 doi
035 $a 978-1-4842-6992-3
050 4 $a QA76.76.M52
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
100 1 $a Elliott, Ed. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1355658
245 1 0 $a Introducing .NET for Apache Spark $h [electronic resource] : $b Distributed Processing for Massive Datasets / $c by Ed Elliott.
250 $a 1st ed. 2021.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2021.
300 $a XV, 262 p. 41 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Part I. Getting Started -- 1. Understanding Apache Spark -- 2. Setting up Spark -- 3 -- Programming with .NET for Apache Spark -- Part II. The APIs -- 4. User-Defined Functions -- 5. The DataFrame API -- 6. Spark SQL and Hive Tables -- 7. Spark Machine Learning API -- Part III. Examples -- 8. Batch Mode Processing -- 9. Structured Streaming -- 10. Troubleshooting -- 11. Delta Lake -- Part IV. Appendices -- Appendix A. Running in the Cloud -- Appendix B. Implementing .NET for Apache Spark Code.
520 $a Get started using Apache Spark via C# or F# and the .NET for Apache Spark bindings. This book is an introduction to both Apache Spark and the .NET bindings. Readers new to Apache Spark will get up to speed quickly using Spark for data processing tasks performed against large and very large datasets. You will learn how to combine your knowledge of .NET with Apache Spark to bring massive computing power to bear by distributed processing of extremely large datasets across multiple servers. This book covers how to get a local instance of Apache Spark running on your developer machine and shows you how to create your first .NET program that uses the Microsoft .NET bindings for Apache Spark. Techniques shown in the book allow you to use Apache Spark to distribute your data processing tasks over multiple compute nodes. You will learn to process data using both batch mode and streaming mode so you can make the right choice depending on whether you are processing an existing dataset or are working against new records in micro-batches as they arrive. The goal of the book is leave you comfortable in bringing the power of Apache Spark to your favorite .NET language. You will: Install and configure Spark .NET on Windows, Linux, and macOS Write Apache Spark programs in C# and F# using the .NET bindings Access and invoke the Apache Spark APIs from .NET with the same high performance as Python, Scala, and R Encapsulate functionality in user-defined functions Transform and aggregate large datasets Execute SQL queries against files through Apache Hive Distribute processing of large datasets across multiple servers Create your own batch, streaming, and machine learning programs.
650 0 $a Microsoft software. $3 1253736
650 0 $a Microsoft .NET Framework. $3 565417
650 0 $a Big data. $3 981821
650 1 4 $a Microsoft and .NET. $3 1114109
650 2 4 $a Big Data. $3 1017136
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484269916
776 0 8 $i Printed edition: $z 9781484269930
856 4 0 $u https://doi.org/10.1007/978-1-4842-6992-3
912 $a ZDB-2-CWD
912 $a ZDB-2-SXPC
950 $a Professional and Applied Computing (SpringerNature-12059)
950 $a Professional and Applied Computing (R0) (SpringerNature-43716)