Description
Practical Apache Spark, 1st ed.
Using the Scala API
Authors: Chellappan Subhashini, Ganesan Dharanitharan
Language: EnglishSubjects for Practical Apache Spark:
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Approximative price 63.29 €
In Print (Delivery period: 15 days).
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Description
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- Discover the functional programming features of Scala
- Understand the complete architecture of Spark and its components
- Integrate Apache Spark with Hive and Kafka
- Use Spark SQL, DataFrames, and Datasets to process data using traditional SQL queries
- Work with different machine learning concepts and libraries using Spark's MLlib packages
Chapter 1: Scala - Functional Programming Aspects
Chapter 2: Single & Multi-node cluster setup
Chapter 3: Introduction to Apache Spark and Spark CoreChapter 4: Spark SQL, Dataframes & Datasets
Chapter 5: Introduction to Spark StreamingChapter 6: Spark Structured Streaming
Chapter 7: Spark Streaming with KafkaChapter 8: Spark Machine Learning Library
Chapter 9: Working with SparkRChapter 10: Spark - Real time use case
Subhashini Chellappan is a technology enthusiast with expertise in the big data and cloud space. She has rich experience in both academia and the software industry. Her areas of interest and expertise are centered on business intelligence, big data analytics and cloud computing.
Dharanitharan Ganesan is a senior analyst with five years of experience in IT. He has a high level of exposure and experience in big data – Apache Hadoop, Apache Spark and various Hadoop ecosystem components. He has a proven track record of improving efficiency and productivity through the automation of various routine and administrative functions in business intelligence and big data technologies. His areas of interest and expertise are centered on machine learning algorithms, statistical modelling and predictive analysis.
Contains extensive coverage of machine-learning algorithms with real-time code implementation using Spark MLib
Explains the SparkR real-time module with code implementation
Covers Spark Streaming and Spark Integration examples with other big data components such as Kafka
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