Apache Spark 4.0 marks a major milestone with advancements in SQL language enhancements, Spark Connect, reliability, Python capabilities, and structured streaming. It's designed to be more powerful, ANSI-compliant, and user-friendly while maintaining compatibility.
This article details how to accelerate deep learning and LLM inference using Apache Spark, focusing on distributed inference strategies. It covers basic deployment with `predict_batch_udf`, advanced deployment with inference servers like NVIDIA Triton and vLLM, and deployment on cloud platforms like Databricks and Dataproc. It also provides guidance on resource management and configuration for optimal performance.
NVIDIA's Project Aether automates the qualification, testing, configuration, and optimization of Spark workloads for GPU acceleration, enabling enterprises to process data more efficiently and cost-effectively.
Learn how GPU acceleration can significantly speed up JSON processing in Apache Spark, reducing runtime and costs for enterprise data applications.
Performance Observability for Apache Spark. DataFlint is an open-source D-APM (Data-Application Performance Monitoring) tool for Apache Spark, built for big data engineers.
