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Distributed Data Systems with Azure Databricks

You're reading from   Distributed Data Systems with Azure Databricks Create, deploy, and manage enterprise data pipelines

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Product type Paperback
Published in May 2021
Publisher Packt
ISBN-13 9781838647216
Length 414 pages
Edition 1st Edition
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Author (1):
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Alan Bernardo Palacio Alan Bernardo Palacio
Author Profile Icon Alan Bernardo Palacio
Alan Bernardo Palacio
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Introducing Databricks
2. Chapter 1: Introduction to Azure Databricks FREE CHAPTER 3. Chapter 2: Creating an Azure Databricks Workspace 4. Section 2: Data Pipelines with Databricks
5. Chapter 3: Creating ETL Operations with Azure Databricks 6. Chapter 4: Delta Lake with Azure Databricks 7. Chapter 5: Introducing Delta Engine 8. Chapter 6: Introducing Structured Streaming 9. Section 3: Machine and Deep Learning with Databricks
10. Chapter 7: Using Python Libraries in Azure Databricks 11. Chapter 8: Databricks Runtime for Machine Learning 12. Chapter 9: Databricks Runtime for Deep Learning 13. Chapter 10: Model Tracking and Tuning in Azure Databricks 14. Chapter 11: Managing and Serving Models with MLflow and MLeap 15. Chapter 12: Distributed Deep Learning in Azure Databricks 16. Other Books You May Enjoy

Scheduling jobs with Azure Databricks

If we already know that the file we want to process will be delivered to the blob storage, we can directly schedule the notebook to run periodically. To do this, we can use Azure Databricks jobs, which is an easy way to schedule the runs of our notebooks. We will suppose now that the file path of the file we will consume is fixed.

Scheduling a notebook as a job

The steps are as follows:

  1. To schedule a new job, click on the Jobs tab in the left ribbon of our workspace and then click on Create Job, as illustrated in the following screenshot:

    Figure 3.34 – Creating an Azure Databricks job

  2. After this, the rest is quite straightforward. We will be required to specify which notebook we will use, set up an execution schedule, and specify the computational resources we will use to execute the job. In this case, we have chosen to run the job in an existing cluster, but we can create a dedicated cluster for specific executions. We...
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