Olivier Soucy’s Post

⚡ Spark – Creating Custom UDFs and Using Pandas UDFs for Scalability 🐼🚀 Need to run custom Python logic on your Spark DataFrame? Spark lets you define UDFs (User Defined Functions)—and for speed, you can use Pandas UDFs to process data in vectorized batches. ⚡ 💡 How? See code snippet below 🔗 Why Pandas UDFs? - Process data in vectorized batches, avoiding Python overhead - Works great for numeric-heavy transformations - Keeps your logic scalable while still writing in Python 💡 Pro Tip: Use Pandas UDFs over standard UDFs whenever possible—they can give a massive performance booston large datasets. 🔹 Follow me for daily DataFrame manipulation tips and other great data engineering content!