# LanceDB Lakehouse multimodal nativo de IA para organizar dados, criar features, pesquisar e preparar datasets de treino - Findings24: - Site: - Preços: Freemium - Categorias: Base de Conhecimento IA, Ferramentas Dev IA, Analista de dados com IA ## O que é LanceDB? LanceDB is an AI-native multimodal lakehouse for teams building training datasets, features, and retrieval systems from diverse data. The site positions it as a unified foundation for curation, feature engineering, search, and training, with support for structured metadata, embeddings, and raw content in the same workflow. The product is described as a way to move from experimentation to production without relying on brittle preprocessing scripts or a collection of disconnected tools. Its enterprise offering combines search, exploratory data analysis, feature engineering, and training, and the accompanying `geneva` Python package exposes a developer-friendly API for defining and running data logic. ## O que LanceDB pode fazer? ### Unified multimodal data layer Store and work with raw bytes, enriched features, metadata, and embeddings in the same table so teams do not need separate systems for each data type. ### Single platform for AI data workflows Run curation, feature engineering, retrieval, and training from one platform instead of stitching together data sync jobs, ad hoc scripts, and separate feature stores. ### Python-based feature engineering Use declarative Python UDFs, including scalar, batched, and stateful functions, to define feature logic and run it across distributed infrastructure. ### Versioned dataset management Update embeddings, add columns, version writes, branch experiments, and roll back datasets without duplicating data or rewriting tables. ### Hybrid search and SQL exploration Combine vector, full-text, and hybrid search with SQL filters for retrieval and exploration against the same table. ### Distributed execution at scale Scale workloads across CPUs and GPUs with Ray and KubeRay for backfills, preprocessing, feature generation, and inference-related jobs. ## Casos de uso ### Training data curation Prepare large training datasets by deduplicating rows, identifying edge cases for labeling, and keeping data preparation and training in the same system. ### Feature engineering pipelines Define Python feature logic locally, then apply it at scale with automatic updates, versioning, and backfills for feature pipelines. ### Search and retrieval systems Run vector, full-text, and hybrid retrieval on a single table for production search, RAG, and other agentic retrieval workflows. ### Dataset exploration and experimentation Use the same platform for exploratory data analysis and production dataset iteration, so teams can compare variants and branch experiments without duplicating data. ### Distributed AI processing Scale preprocessing, inference-related jobs, or other distributed workloads across CPUs and GPUs using Ray and KubeRay. ## Perguntas frequentes ### What is LanceDB? LanceDB is presented as an AI-native multimodal lakehouse that combines search, exploratory data analysis, feature engineering, and training in one platform. The source materials emphasize unified access to raw data, embeddings, metadata, and SQL-based exploration. ### How do teams work with it? The sources describe the Multimodal Lakehouse suite as part of LanceDB Enterprise and note that it uses the \`geneva\` Python package for a developer-friendly API. The blog also says users can define feature logic as standard Python functions, including UDFs. ### What workflows does it support? The homepage highlights curation, feature engineering, training, and search and retrieval. It also mentions vector search, full-text search, hybrid search, SQL filters, automatic versioning, incremental updates, and distributed execution. ### How is it priced? The pricing page captured in the sources is a contact form rather than a public pricing table. It asks visitors to fill out the form and says the team will respond within 48 hours. ### Does LanceDB mention compliance support? The download page states enterprise-grade compliance claims for SOC 2 Type II, GDPR, and HIPAA. Those claims appear on the resource page rather than on a detailed trust-center page in the provided sources. ## Informações rápidas - **Category**: Multimodal lakehouse for AI - **Primary users**: Data scientists, ML engineers, and AI teams - **Platform**: Enterprise product with a Python API - **Source domain**: lancedb.com - **Notable workflows**: Curation, feature engineering, search, retrieval, and training - **Pricing signal**: Contact sales / request information