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Apache Spark 2: Data Processing and Real-Time Analytics

You're reading from   Apache Spark 2: Data Processing and Real-Time Analytics Master complex big data processing, stream analytics, and machine learning with Apache Spark

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Product type Course
Published in Dec 2018
Publisher Packt
ISBN-13 9781789959208
Length 616 pages
Edition 1st Edition
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Authors (7):
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Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
Romeo Kienzler Romeo Kienzler
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Romeo Kienzler
Siamak Amirghodsi Siamak Amirghodsi
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Siamak Amirghodsi
Broderick Hall Broderick Hall
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Broderick Hall
Md. Rezaul Karim Md. Rezaul Karim
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Md. Rezaul Karim
Meenakshi Rajendran Meenakshi Rajendran
Author Profile Icon Meenakshi Rajendran
Meenakshi Rajendran
Shuen Mei Shuen Mei
Author Profile Icon Shuen Mei
Shuen Mei
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Table of Contents (23) Chapters Close

Title Page
Copyright
About Packt
Contributors
Preface
1. A First Taste and What's New in Apache Spark V2 FREE CHAPTER 2. Apache Spark Streaming 3. Structured Streaming 4. Apache Spark MLlib 5. Apache SparkML 6. Apache SystemML 7. Apache Spark GraphX 8. Spark Tuning 9. Testing and Debugging Spark 10. Practical Machine Learning with Spark Using Scala 11. Spark's Three Data Musketeers for Machine Learning - Perfect Together 12. Common Recipes for Implementing a Robust Machine Learning System 13. Recommendation Engine that Scales with Spark 14. Unsupervised Clustering with Apache Spark 2.0 15. Implementing Text Analytics with Spark 2.0 ML Library 16. Spark Streaming and Machine Learning Library 1. Other Books You May Enjoy Index

Overview


The following diagram shows potential data sources for Apache Streaming, such as Kafka, Flume, and HDFS:

These feed into the Spark Streaming module and are processed as Discrete Streams. The diagram also shows that other Spark module functionality, such as machine learning, can be used to process stream-based data.

The fully processed data can then be an output for HDFS, databases, or dashboards. This diagram is based on the one at the Spark streaming website, but we wanted to extend it to express the Spark module functionality:

When discussing Spark Discrete Streams, the previous figure, taken from the Spark website at http://spark.apache.org/, is the diagram that we would like to use.

The green boxes in the previous figure show the continuous data stream sent to Spark being broken down into a Discrete Stream (DStream).

Note

A DStream is nothing other than an ordered set of RDDs. Therefore, Apache Spark Streaming is not real streaming, but micro-batching. The size of the RDDs backing...

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