Supercharging Iceberg with DataFusion Comet Unlocking Vectorized Execution in Spark (OpenAI)

Given the extensive adoption of Spark as a primary compute engine for Apache Iceberg operations, optimizing Spark's performance emerges as a pivotal goal. Apache DataFusion Comet, a plugin to leverage Apache DataFusion, stands at the forefront of this endeavor, promising to enhance Spark's operational efficiency substantially. Comet as a Spark plugin aims to supercharge Spark workloads by delegating the compute to native vectorized DataFusion kernels from Spark’s traditional JVM-based SQL execution engine. This innovative approach not only elevates performance but also introduces efficiency gains for a variety of workloads. During our session, we will delve into the mechanics of Apache DataFusion Comet, exploring its architecture, performance benefits, and our strategy for a seamless integration with Apache Iceberg, thereby unlocking new potentials for data processing and analytics.