Data analytics with Spark using Python /

Spark is at the heart of today’s big data revolution, helping data professionals supercharge efficiency and performance in a wide range of data processing and analytics tasks. In this guide, big data expert Jeffrey aven covers all students need to know to leverage Spark, together with its extensions...

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Hlavní autor: Aven, Jeffrey
Médium: Printed Book
Vydáno: Noida: Pearson, 2019.
Témata:
Electronic data processing
Big data.
Python (Computer program language)
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245 1 0 |a Data analytics with Spark using Python / 
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300 |a x, 306 pages :  |b illustrations ; 
500 |a Includes index. 
505 |a Chapter 1 introducing big data, Hadoop and Spark Chapter 2 deploying Spark Chapter 3 Understanding the Spark cluster architecture Chapter 4 Learning Spark programming basics part II: Beyond the basics Chapter 5 Advanced programming using the Spark core API Chapter 6 SQL and nose programming with Spark Chapter 7 stream processing and messaging using Spark Chapter 7 stream processing and messaging using Spark. 
520 |a Spark is at the heart of today’s big data revolution, helping data professionals supercharge efficiency and performance in a wide range of data processing and analytics tasks. In this guide, big data expert Jeffrey aven covers all students need to know to leverage Spark, together with its extensions, subprojects and wider ecosystem. Aven combines a language-agnostic introduction to foundational Spark concepts with extensive programming examples utilizing the popular and intuitive pyspark development environment. This guided focus on Python makes it widely accessible to students at various levels of experience those with little Hadoop or Spark experience. Aven’s broad coverage ranges from basic to advanced Spark programming and Spark SQL to machine learning. Students will learn how to efficiently manage all forms of data with Spark: Streaming, structured, semi-structured and unstructured. Throughout, concise topic overviews quickly get you up to speed and extensive hands-on exercises prepare you to solve real problems coverage includes: Understand spark’s evolving role in the big data and Hadoop ecosystems create Spark clusters using various deployment modes control and optimize the operation of Spark clusters and applications master Spark core RDD API programming techniques extend, accelerate and optimize Spark routines with advanced API platform constructs, including shared variables, RDD storage and partitioning efficiently integrate Spark with both SQL and nonrelational data stores perform stream processing and messaging with Spark streaming and Apache Kafka implement predictive modeling with sparkr and Spark table of Contents: mllibpart I: Spark foundations  
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650 0 |a Big data. 
650 0 |a Python (Computer program language) 
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