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Learning Spark SQL

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

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781785888359
Length 452 pages
Edition 1st Edition
Languages
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Author (1):
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Aurobindo Sarkar Aurobindo Sarkar
Author Profile Icon Aurobindo Sarkar
Aurobindo Sarkar
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Table of Contents (13) Chapters Close

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

Munging time series data


Time series data is a sequence of linked to a timestamp. In section, we use Cloudera's spark-ts package for analyzing time-series data.

Note

Refer to Cloudera Engineering Blog, A New Library for Analyzing Time-Series Data with Apache Spark, for more details on time-series data and its processing using spark-ts. This blog is available at: https://github.com/sryza/spark-timeseries.

The spark-ts package can be downloaded and using instructions available at:

https://github.com/sryza/spark-timeseries.

We will attempt to accomplish the following objectives in the following sub-sections:

  • Pre-processing of the time-series Dataset
  • Processing date fields
  • Persisting and loading data
  • Defining a date-time index
  • Using the  TimeSeriesRDD object
  • Handling missing time-series data
  • Computing basic statistics

For this section, specify inclusion of the spark-ts.jar file while starting the Spark shell as shown:

We download Datasets containing pricing and volume data for six stocks over a one year...

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