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Modern Data Architectures with Python

You're reading from   Modern Data Architectures with Python A practical guide to building and deploying data pipelines, data warehouses, and data lakes with Python

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781801070492
Length 318 pages
Edition 1st Edition
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Author (1):
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Brian Lipp Brian Lipp
Author Profile Icon Brian Lipp
Brian Lipp
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Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1:Fundamental Data Knowledge
2. Chapter 1: Modern Data Processing Architecture FREE CHAPTER 3. Chapter 2: Understanding Data Analytics 4. Part 2: Data Engineering Toolset
5. Chapter 3: Apache Spark Deep Dive 6. Chapter 4: Batch and Stream Data Processing Using PySpark 7. Chapter 5: Streaming Data with Kafka 8. Part 3:Modernizing the Data Platform
9. Chapter 6: MLOps 10. Chapter 7: Data and Information Visualization 11. Chapter 8: Integrating Continous Integration into Your Workflow 12. Chapter 9: Orchestrating Your Data Workflows 13. Part 4:Hands-on Project
14. Chapter 10: Data Governance 15. Chapter 11: Building out the Groundwork 16. Chapter 12: Completing Our Project 17. Index 18. Other Books You May Enjoy

Terraform

Databricks supports workflows in Terraform, and it’s a very viable way to deploy and change your workflows.

Here is how you can define a workflow, also called a job in some interfaces. You must set your workflow name and the resource name. After that, you must define tasks within the workflow:

resource "databricks_job" "my_pipeline_1" {
 name = "my_awsome_pipeline"
   task {
....
       existing_cluster_id = <cluster-id>
   }
      task {
....
       existing_cluster_id = <cluster-id>
   }
}

Failed runs

When your workflows fail, you have the option to repair your run. You don’t need to rerun the whole pipeline, and Workflows is smart enough to just run your failed steps. This brings up the important topic of creating idempotent steps in a workflow. In short, if you...

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