<!channel> ✅ No JVMs ✅ No JARs ✅ Local or Distributed ✅O ne engine for all Daft Launch Week Day 3 🚀 Spark Connect for Daft Tired of juggling JVM, JARs, and legacy pipelines just to run Spark? 😭 What if your existing PySpark code “just worked”... but without Spark…? With Spark Connect for Daft, you can now: ⚡ Run the SAME Spark queries using Daft’s blazing-fast engine ⚙️ Switch from PySpark to Daft with just 2 lines of code: daft.pyspark and .local() or .remote() 🌍 Scale from local to distributed with Ray—no JVM required ❌ No need to maintain separate stacks for both legacy and multimodal workloads Same Spark code, radically better experience. Multimodal + traditional data under one unified engine 💜 Follow along as @Kevin Wang, Software Engineer at Eventual, shows you just how easy it is. It’s fast. It’s simple. Try it out yourself:

pip install "daft[spark]"

Learn more about Spark Connect in our docs: https://docs.getdaft.io/en/stable/spark_connect/