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Learning PySpark

You're reading from   Learning PySpark Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781786463708
Length 274 pages
Edition 1st Edition
Languages
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Authors (2):
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Denny Lee Denny Lee
Author Profile Icon Denny Lee
Denny Lee
Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
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Toc

Table of Contents (13) Chapters Close

Preface 1. Understanding Spark FREE CHAPTER 2. Resilient Distributed Datasets 3. DataFrames 4. Prepare Data for Modeling 5. Introducing MLlib 6. Introducing the ML Package 7. GraphFrames 8. TensorFrames 9. Polyglot Persistence with Blaze 10. Structured Streaming 11. Packaging Spark Applications Index

Databricks Jobs

If you are using the Databricks product, an easy way to go from development from your Databricks notebooks to production is to use the Databricks Jobs feature. It will allow you to:

  • Schedule your Databricks notebook to run on an existing or new cluster
  • Schedule at your desired frequency (from minutes to months)
  • Schedule time out and retries for your job
  • Be alerted when the job starts, completes, and/or errors out
  • View historical job runs as well as review the history of the individual notebook job runs

This capability greatly simplifies the scheduling and production workflow of your job submissions. Note that you will need to upgrade your Databricks subscription (from Community edition) to use this feature.

To use this feature, go to the Databricks Jobs menu and click on Create Job. From here, fill out the job name and then choose the notebook that you want to turn into a job, as shown in the following screenshot:

Databricks Jobs

Once you have chosen your notebook, you can also choose whether to...

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