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Apache Spark Fundamentals and CookBook

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Book Title: Apache Spark Fundamental Concepts and Cookbook

Subtitle: It includes 35 Hands On Exercises using Scala.

 

About Book

Apache Spark is one of the fastest growing technology in BigData computing world. It support multiple programming languages like Java, Scala, Python and R. Hence, many existing and new framework started to integrate Spark platform as well in their platform e.g. Hadoop, Cassandra, EMR etc. While creating Spark certification material HadoopExam technical team found that there is no proper material and book is available for the Spark SQL (version 2.x) which covers the concepts as well as use of various features and found difficulty in creating the material. Therefore, they decided to create full length book for Spark SQL and outcome of that is this book. In this book technical team try to cover both fundamental concepts of Spark SQL engine and many exercises approx. 35+ so that most of the programming features can be covered.  There are approximately 35 exercises and total 15 chapters which covers the programming aspects of SparkSQL. All the exercises given in this book are written using Scala. However, concepts remain same even if you are using different programming language.

 

Source Code Download

http://hadoopexam.com/books/SparkSQL/Spark_SQL_Edition_1_Data_and_Source.zip

 

About SparkSQL

Spark SQL is a module created on top of Spark Core and introduced a data abstraction called DataFrames or Dataset which provides support for structured and semi-structured data. Spark SQL provides a domain-specific language (DSL) to manipulate DataFrames/Datasets in Scala, Java, or Python (No Dataset in Python). It also provides SQL language support, with command-line interfaces and ODBC/JDBC server. Although DataFrames lack the compile-time type-checking afforded by RDDs, as of Spark 2.0, the strongly typed DataSet is fully supported by Spark SQL as well. More detail about Spark SQL you will find in this book.

Teacher

QuickTechie Learning Resources

 

QuickTechie Learning Resources is part of QuickTechie Technical Team Member. We have been working as part of HadoopExam which was started in 2012 with a single training & certification for Hadoop. That became so popular successful that for a year we focused only on this one product. Then later HadoopExam entered in another popular tech world AWS Cloud Computing, again there was a huge success. Since, then we have not looked back and continuously our portfolio of training & certification is keep increasing. We have a dedicated inhouse technical team, which does market research and bring up new products. HadoopExam main focus is around below products.

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Book Title: Apache Spark Fundamental Concepts and Cookbook

Subtitle: It includes 35 Hands On Exercises using Scala.

 

About Book

Apache Spark is one of the fastest growing technology in BigData computing world. It support multiple programming languages like Java, Scala, Python and R. Hence, many existing and new framework started to integrate Spark platform as well in their platform e.g. Hadoop, Cassandra, EMR etc. While creating Spark certification material HadoopExam technical team found that there is no proper material and book is available for the Spark SQL (version 2.x) which covers the concepts as well as use of various features and found difficulty in creating the material. Therefore, they decided to create full length book for Spark SQL and outcome of that is this book. In this book technical team try to cover both fundamental concepts of Spark SQL engine and many exercises approx. 35+ so that most of the programming features can be covered.  There are approximately 35 exercises and total 15 chapters which covers the programming aspects of SparkSQL. All the exercises given in this book are written using Scala. However, concepts remain same even if you are using different programming language.

 

Source Code Download

http://hadoopexam.com/books/SparkSQL/Spark_SQL_Edition_1_Data_and_Source.zip

 

About SparkSQL

Spark SQL is a module created on top of Spark Core and introduced a data abstraction called DataFrames or Dataset which provides support for structured and semi-structured data. Spark SQL provides a domain-specific language (DSL) to manipulate DataFrames/Datasets in Scala, Java, or Python (No Dataset in Python). It also provides SQL language support, with command-line interfaces and ODBC/JDBC server. Although DataFrames lack the compile-time type-checking afforded by RDDs, as of Spark 2.0, the strongly typed DataSet is fully supported by Spark SQL as well. More detail about Spark SQL you will find in this book.

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