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Posts Tagged ‘#PySpark’

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Writing Testable Python Objects in Databricks

I’ve been writing about Test-Driven Development in Databricks and some of the interesting issues that you can run into with Python objects. It’s always been my opinion that code that is not testable is detestable. Admittedly, its been very difficult getting to where I wanted to be with Databricks and TDD. Unfortunately, it’s hard to […]

Understanding the role of Py4J in Databricks

I mentioned that my attempt to implement TDD with Databricks was not totally successful. Setting up the local environment was not a problem and getting a service id for CI/CD component was more of an administrative than a technical problem. Using mocks to test python objects that are serialized to Spark is actually the issue. […]

Read Azure Eventhub data to DataFrame – Python

Reading Azure EventHub Data into DataFrame using Python in Databricks Azure EventHubs offer a powerful service for processing large amounts of data. In this guide, we’ll explore how to efficiently read data from Azure EventHub and convert it into a DataFrame using Python in Databricks. This walkthrough simplifies the interaction between Azure EventHubs and the […]

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PySpark – Coding Standards & Best Practices

Purpose: The primary objective for this document is to provide awareness and establish clear understanding of coding standards and best practices to adhere while developing PySpark components. Best Practices are any procedure that is accepted as being the most effective either by consensus or prescription. Practices can range from stylistic to in-depth design methodologies. In […]