McLaren Applied and KX have formed a partnership to enhance McLaren Applied's ATLAS platform with KX's kdb+ vector native, time series database. The integration will allow motorsport teams to monitor race data, run AI and ML queries, and perform complex analysis of large datasets in real time, strengthening the capabilities of the ATLAS platform and supporting its expansion into new sectors.
TileDB, an array database, has announced support for vector search, making it a natural choice for delivering vector search functionality. TileDB is faster than popular vector search library FAISS, works with any storage backend, offers serverless computing, and manages various data modalities in a unified solution.
Generative AI algorithms are driving significant changes in the database landscape, with updates occurring at every level of the data storage stack. These changes include storing information as long vectors of numbers, enabling complex queries and recommendation systems, creating indices that span all the values in a vector, classifying data through AI algorithms to impose order on messy datasets, automating higher-level meta-tasks to optimize performance, cleaning and correcting data anomalies, detecting fraud, enhancing security, and even merging the traditional database with generative AI systems for more flexible data storage and querying.
Airbyte has unveiled new vector database connectors that allow users to configure the full Extract-Load-Transform (ELT) pipeline, extracting records from various sources, preparing and embedding text contents of records, and loading them into vector databases through a user-friendly interface. Alation has launched a new data center in Japan, allowing customers to process and store data locally to meet data residency requirements. These are just a few of the top data management news items curated by Solutions Review editors.
In this tutorial, you will learn how to extract unstructured data from different sources and load it efficiently into a vector database. The tutorial also covers how to prepare the data for LLM usage and integrate a vector database, allowing you to ask questions about your proprietary data.