Starburst & Spark

Apache Spark and Starburst are complementary processing engines that, when combined, provide versatile options for managing data workloads, solutions, and applications, particularly in machine learning (ML) and artificial intelligence (AI) workflows. Spark's Python-centric approach is well-suited for tasks involving real-time streaming and machine learning, offering sophisticated frameworks for event processing and scalable ML algorithms. It is particularly adept at handling unstructured data through transformations and integrations with libraries like Unstructured.IO, which are crucial for preparing data for generative AI (GenAI) applications. In contrast, Starburst leverages a SQL-based strategy that excels in handling structured data and interactive querying, making it ideal for traditional data lake activities and generating embeddings from text chunks. Together, Spark and Starburst offer powerful tools for handling both structured and unstructured data within a unified data lakehouse architecture, enabling enterprises to effectively implement AI data strategies tailored to their specific needs.