How do you develop a python library in Microsoft Fabric while maintaining the full ability to test code prior to packaging?

With Microsoft Build 2024 underway, the wave of announcements are hot off the press! This is a recap of some of the data engineering specific updates that I’m particularly excited about.

LLMs like ChatGPT and CoPilot are transforming every industry, so why not use them as a data engineer to free up time for more complex tasks? One thing every data engineer—and most humans—are revolted by is repetitive tasks. Thankfully, we don’t live in the world of iRobot and all we need are tokens to pay the LLM masters to get our work done.

Photon is a native vectorized execution engine within Databricks, entirely written in C++, designed to massively boost performance on top of Spark by circumventing some of the JVM inefficiencies and better leveraging modern hardware.

I recently blogged about cluster configuration options in Spark and how you can maximize compute utilization and processing time. Of the many options that I listed and data provided, I never gave any benchmarks comparing RunMultiple and Multithreading. The goal of this post is exactly that, drilling into real data that pushes the concurrency limits of both. Going forward I’ll reference Multithreading simply as ThreadPools since that is the specific Multithreading implementation that I’ll be testing.