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Modern Data Architectures with Python

You're reading from  Modern Data Architectures with Python

Product type Book
Published in Sep 2023
Publisher Packt
ISBN-13 9781801070492
Pages 318 pages
Edition 1st Edition
Languages
Author (1):
Brian Lipp Brian Lipp
Profile icon Brian Lipp
Toc

Table of Contents (19) Chapters close

Preface 1. Part 1:Fundamental Data Knowledge
2. Chapter 1: Modern Data Processing Architecture 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

Practical lab

Our cloud team will be triggering an AWS Lambda and passing the path to the data being delivered from our ingestion tool. They have asked for a Lambda that will pass that information to your workflow, which should be parameterized. This type of request is very common and allows Databricks to be interacted with using a variety of tooling, such as AWS Step Functions and Jenkins, among others.

Solution

In this solution, we will walk you through the Python code needed to complete the tasks. There are two ways to access Databricks via the REST API – using the requests package, as shown previously, and using the Python package provided by Databricks. In my solution, I am using the Databricks package to keep things simple. I have not come across a case where the package doesn’t meet my needs, but if it’s not good enough, you can always access the REST API directly.

Lambda code

Here, I am importing all my Python libraries. Take note of the databricks_cli...

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