What is CrateDB?

CrateDB provides a robust database solution designed as a unified data layer for managing and analyzing diverse data types in real time. It supports complex queries across various data models including relational, document (JSON), time-series, geospatial, vector, and full-text search, powered by a distributed SQL query engine built on Apache Lucene. The platform is built for scalability and resilience, capable of handling massive data volumes from millions of records to petabytes with high-speed performance and fault tolerance. It ensures data is immediately available for querying upon ingestion and dynamically optimizes indexing for continuous performance.

The system facilitates seamless integration with AI and machine learning models, enabling storage, search, and querying of vectors for real-time training and decision-making. CrateDB emphasizes developer productivity through native SQL support, PostgreSQL compatibility for easy integration with third-party tools, and compatibility with AI/ML tools like LangChain. It offers flexible deployment options including Database-as-a-Service (DBaaS), hybrid cloud, or self-managed setups, adaptable for various environments from single laptops to large server clusters and edge deployments.

Features

Real-Time Data Insights: Access and analyze data in real-time for immediate decision-making.
Turbocharged Aggregations: Execute ad-hoc queries on billions of records in milliseconds using columnar storage.
Hybrid Search Powered by Lucene: Combine full-text, geospatial, vector similarity, and relational searches across complex datasets.
Seamless AI Model Integration: Store, search, and query vectors to integrate with AI/ML models for real-time training and predictions.
Dynamic Schema and Indexing: Adapt to changing data structures without downtime, with automatic index optimization.
Scalability and Resilience: Manage petabyte-scale data with high-speed performance, fault tolerance, and automatic failover/recovery.
Multi-Model Data Support: Handle time-series, document/JSON, vector, full-text, spatial, and relational data within a single system.
PostgreSQL Compatibility: Integrate easily with existing tools and ecosystems.
Flexible Deployment: Deploy as DBaaS, hybrid cloud, self-managed, or on edge devices.

Use Cases

Real-time analytics dashboards
Hybrid search applications combining diverse data types
AI/ML model integration and vector storage/search
Building AI chatbots with real-time data access
Internet of Things (IoT) data ingestion and analysis
Geospatial analytics and location-based services
Log and event analysis for monitoring and security
Data-driven predictive maintenance
Real-time production monitoring

FAQs

How can CrateDB be deployed?

CrateDB offers flexible deployment options including Database-as-a-Service (DBaaS) on CrateDB Cloud, Private or Public Cloud, On-Premises, or Edge environments.
Are hybrid deployments supported?

Yes, you can connect clusters from any cloud provider, on-premises infrastructure, or edge locations to CrateDB Cloud plans.
How is data privacy and security managed?

Data can be stored in preferred cloud regions. Security features include always-on encryption (in-flight and at-rest), access restrictions, private links, integrated user management, ISO 27001 Certification, and SOC 2 Type 2 Certification.
What data models does CrateDB support?

CrateDB supports multiple data models including time-series, document/JSON, vector, full-text, spatial, and relational data within a single system.
Is CrateDB compatible with other tools?

Yes, CrateDB offers PostgreSQL compatibility for easy integration with third-party tools and supports integration with AI/ML tools like LangChain.