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You're reading from Apache Spark 2.x Machine Learning Cookbook

Product typeBook

Published inSep 2017

Reading LevelIntermediate

PublisherPackt

ISBN-139781783551606

Edition1st Edition

Languages

Scala

Tools

Apache Spark

Concepts

Machine Learning

Authors (5):

Mohammed Guller

Siamak Amirghodsi

Shuen Mei

Meenakshi Rajendran

Broderick Hall

View More author details

Streaming data and debugging with queueStream

In this recipe, we the concept of queueStream(), which is a valuable tool while trying to get a streaming program to work during the development cycle. We found the queueStream() API very useful and felt that other developers can benefit from a recipe that fully its usage.

We start by simulating a user browsing various URLs associated with different web pages using the program ClickGenerator.scala and then proceed to consume and tabulate the data (user behavior/visits) using the ClickStream.scala program:

We use Spark's streaming API with Dstream(), which will require the use of a streaming context. We are calling this out explicitly to highlight one of the differences between Spark streaming and the Spark structured streaming programming model.

Note

There are two distinct programs (ClickGenerator.scala and ClickStream.scala) that make up this recipe.

How to do it...

Start a new project in IntelliJ or in an IDE of your choice. Make sure that the...

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Apache Spark 2.x Machine Learning Cookbook

Published in: Sep 2017Publisher: PacktISBN-13: 9781783551606

Authors (5)

Mohammed Guller

Author of Big Data Analytics with Spark - http://www.apress.com/9781484209653
Read more about Mohammed Guller

Siamak Amirghodsi

Siamak Amirghodsi (Sammy) is interested in building advanced technical teams, executive management, Spark, Hadoop, big data analytics, AI, deep learning nets, TensorFlow, cognitive models, swarm algorithms, real-time streaming systems, quantum computing, financial risk management, trading signal discovery, econometrics, long-term financial cycles, IoT, blockchain, probabilistic graphical models, cryptography, and NLP.
Read more about Siamak Amirghodsi

Shuen Mei

Shuen Mei is a big data analytic platforms expert with 15+ years of experience in designing, building, and executing large-scale, enterprise-distributed financial systems with mission-critical low-latency requirements. He is certified in the Apache Spark, Cloudera Big Data platform, including Developer, Admin, and HBase. He is also a certified AWS solutions architect with emphasis on peta-byte range real-time data platform systems.
Read more about Shuen Mei

Meenakshi Rajendran

Meenakshi Rajendran is experienced in the end-to-end delivery of data analytics and data science products for leading financial institutions. Meenakshi holds a master's degree in business administration and is a certified PMP with over 13 years of experience in global software delivery environments. Her areas of research and interest are Apache Spark, cloud, regulatory data governance, machine learning, Cassandra, and managing global data teams at scale.
Read more about Meenakshi Rajendran

Broderick Hall

Broderick Hall is a hands-on big data analytics expert and holds a masters degree in computer science with 20 years of experience in designing and developing complex enterprise-wide software applications with real-time and regulatory requirements at a global scale. He is a deep learning early adopter and is currently working on a large-scale cloud-based data platform with deep learning net augmentation.
Read more about Broderick Hall

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You're reading from Apache Spark 2.x Machine Learning Cookbook

Streaming data and debugging with queueStream

Note

How to do it...

Unlock this book and the full library FREE for 7 days

Authors (5)

Hands-On Big Data Analytics with PySpark

Machine Learning with Spark

Spark ML is the machine learning module of Spark. It uses in-memory RDDs to process machine learning models faster for clustering, classification, and regression.

Hands-On Data Analysis with Scala

This book will help you perform effective data analysis with Scala using practical examples. You will come across different challenges and their effective solutions for a variety of data processing tasks - be it data exploration, data manipulation, or real-time data analysis using Apache Spark.

Apache Spark Quick Start Guide

Modern Scala Projects

PySpark Cookbook

Learning Apache Spark 2

Machine Learning with Scala Quick Start Guide

Scala and Spark for Big Data Analytics

Apache Spark 2.x for Java Developers

Learning Spark SQL

Apache Spark 2.x Cookbook

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