WeRide, a global leader in autonomous driving technologies, faced challenges in efficiently mining data to address long-tail scenarios critical to autonomous vehicle performance. To tackle these challenges, they sought a data platform capable of rapid, cost-efficient searches across complex, multi-modal datasets. Traditional SQL-based solutions proved inadequate, prompting WeRide to adopt LanceDB, a vector database that supports multi-modal data and fast search capabilities with metadata filtering. This strategic decision enabled WeRide to significantly enhance their data analysis process, achieving a 90x improvement in ML developer productivity and a 3x reduction in ML training time. LanceDB's seamless scalability and ease of maintenance have been instrumental in supporting WeRide's growing data needs, underpinning future innovation and business growth.