PySpark has long been a cornerstone of big data processing, excelling in data preparation, analytics and machine learning tasks within traditional data lakes. However, the rise of multimodal AI and vector search introduces challenges beyond its capabilities. Spark’s new Python data source API enables integration with emerging AI data lakes built on the multi-modal Lance format. Lance delivers unparalleled value with its zero-copy schema evolution capability and robust support for large record-size data (e.g., images, tensors, embeddings, etc), simplifying multimodal data storage. Its advanced indexing for semantic and full-text search, combined with rapid random access, enables high-performance AI data analytics to the level of SQL. By unifying PySpark's robust processing capabilities with Lance's AI-optimized storage, data engineers and scientists can efficiently manage and analyze the diverse data types required for cutting-edge AI applications within a familiar big data framework.

If you're building AI applications, chances are you're solving a retrieval problem somewhere along the way. This is why vector databases are popular today. But if we zoom out from just vector search, serving AI applications also requires handling KV workloads like a traditional feature store, as well as analytical workloads to explore and visualize data. This means that building an AI application often requires multiple data stores, which means multiple data copies, manual syncing, and extra infrastructure expenses. LanceDB is the first and only system that supports all of these workloads in one system. Powered by Lance columnar format, LanceDB completely breaks open the impossible triangle of performance, scalability, and cost for AI serving. Serving AI applications is different from previous waves of technology, and a new paradigm demands new tools.