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You're reading from Learning Spark SQL Architect streaming analytics and machine learning solutions

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781785888359

Length 452 pages

Edition 1st Edition

Languages

Scala

Tools

Apache Spark

Concepts

Data Streaming

Author (1):

Sarkar

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Table of Contents (19) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Getting Started with Spark SQL FREE CHAPTER

2. Using Spark SQL for Processing Structured and Semistructured Data

3. Using Spark SQL for Data Exploration

4. Using Spark SQL for Data Munging

5. Using Spark SQL in Streaming Applications

6. Using Spark SQL in Machine Learning Applications

7. Using Spark SQL in Graph Applications

8. Using Spark SQL with SparkR

9. Developing Applications with Spark SQL

10. Using Spark SQL in Deep Learning Applications

11. Tuning Spark SQL Components for Performance

12. Spark SQL in Large-Scale Application Architectures

Understanding the Lambda architecture

The Lambda architectural pattern attempts to combine the best of worlds--batch processing and stream processing. This pattern consists of several layers: Batch Layer (ingests and processes data on persistent storage such as HDFS and S3), Speed Layer (ingests processes streaming data that has not been processed by the Batch Layer yet), and the Serving Layer (combines outputs from the Batch and Speed Layers to present merged results). This is a popular architecture in Spark environments because it can support both the Batch and Speed Layer implementations with minimal code differences between the two.

The given figure depicts the Lambda architecture as a combination of batch processing and stream processing:

The next figure an implementation the Lambda architecture AWS services (Amazon Kinesis, Amazon S3 Storage, Amazon EMR, Amazon DynamoDB, and so on) and Spark: