databendlabs/databend

Features

• Cloud-Native Databases - Functions as a cloud-native database designed for elastic scaling and independent compute and storage management.
• Serverless Databases - Provides a serverless database experience that automatically scales compute resources and supports scaling to zero.
• Columnar Storage Engines - Implements a storage engine that organizes data by column to optimize high-volume analytical read performance.
• Data Analytics Engines - Executes complex analytical queries over massive datasets to generate business intelligence insights.
• Analytics Data Platforms - Serves as a centralized platform for large-scale data aggregation and insight generation via a cloud-native warehouse.
• Serverless Warehouses - Implements a serverless data warehouse architecture that scales compute automatically and separates it from storage.
• Stateless Compute Scaling - Scales compute resources independently of storage to minimize costs during idle periods.
• Object Storage Persistence - Persists data in cloud object storage to decouple compute from storage for independent scaling.
• OLAP Database Engines - Provides an OLAP database engine optimized for complex analytical queries and aggregate summaries.
• SQL Engines - Implements a SQL-compliant engine that manages complex query execution over large-scale cloud storage.
• Vector Databases - Integrates a vector database capable of indexing high-dimensional embeddings for semantic search.
• Vector Indexing - Provides high-dimensional indexing structures to enable fast similarity searches across embedding vectors.
• Vector Search - Provides high-dimensional vector search capabilities for semantic and hybrid retrieval across massive datasets.
• Analytics Engines - Offers a serverless analytics platform that automatically scales compute resources based on real-time demand.
• Schema Evolution - Automatically adjusts table schemas to accommodate changes in incoming data structures without manual intervention.
• Data Versioning - Creates snapshots and branches of production data to enable experimentation and testing without affecting primary datasets.
• Hybrid Vector-Keyword Indexing - Combines semantic vector embeddings with keyword matching to provide hybrid information retrieval.
• Storage Tiering - Moves data between different storage tiers based on access frequency to balance performance and cost.
• Relational Vector Engines - Unifies relational SQL analytics with vector similarity search to filter results using structured metadata.
• Vector Similarity Search - Performs semantic search by comparing vector embeddings using mathematical distance metrics.
• Enterprise Data Governance - Enforces role-based access control and data masking to ensure security and compliance across large datasets.
• Enterprise Security Controls - Enforces enterprise-grade security through role-based access control, data masking, and compliance audit logs.
• Shared-Nothing Processing Engines - Distributes query processing across independent worker nodes to ensure high performance and availability.
• Database Systems - Cloud-native DBMS for real-time data processing and analytics.

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