Built for Agents: The Case for Spark Declarative Pipelines

This post introduces and advocates for the use of declarative pipelines for building data pipelines, especially for AI agents. It presents benchmarking data comparing declarative pipelines (built on Spark) against procedural code, showing that declarative pipelines are cheaper, faster, and leaner. Key technical arguments include the ability to validate declarative pipelines before execution and the offloading of boilerplate tasks to Spark. The post details the testing methodology, including the number of pipelines tested and the metrics used (cost, speed, resource utilization).