IMPORTANT COVID-19 UPDATES
9780134846019

Data Analytics with Spark Using Python

  • ISBN 13:

    9780134846019

  • ISBN 10:

    013484601X

  • Format: Paperback
  • Copyright: 06/06/2018
  • Publisher: Addison-Wesley Professional

Note: Not guaranteed to come with supplemental materials (access cards, study guides, lab manuals, CDs, etc.)

Extend Your Rental at Any Time

Need to keep your rental past your due date? At any time before your due date you can extend or purchase your rental through your account.

Sorry, this item is currently unavailable.

Summary

Solve Data Analytics Problems with Spark, PySpark, and Related Open Source Tools

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 you 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 guide’s focus on Python makes it widely accessible to large audiences of data professionals, analysts, and developers—even those with little Hadoop or Spark experience.

Aven’s broad coverage ranges from basic to advanced Spark programming, and Spark SQL to machine learning. You’ll 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 MLlib

Author Biography

Read more

Table of Contents

Read more

Write a Review