RAG Database: What It Is, Why It Matters, and How to Choose the Right One
What is a RAG database? Learn how retrieval-augmented generation works, why traditional databases fall short, and what to look for in a RAG-ready data platform.
What is a RAG database? Learn how retrieval-augmented generation works, why traditional databases fall short, and what to look for in a RAG-ready data platform.
Learn what an edge database is, why it matters for IoT and real time AI, how it fits into modern architectures, and how CrateDB delivers edge intelligence.
Learn what defines a modern analytics database, why traditional systems fall short, and how CrateDB delivers real-time insight at scale for AI-driven organizations.
Explore how a unified database approach simplifies modern data architectures by handling time series, geospatial, JSON, vectors, logs, and AI workloads in one system. Learn how CrateDB reduces complexity and delivers real time analytics at scale.
Discover the real engineering challenges HTAP solves, from ingestion delays to reindexing pain and workload isolation. Learn how unified engines like CrateDB simplify real time data.
Discover how vector databases revolutionize AI by enabling semantic search and real-time analytics, and learn how CrateDB integrates these capabilities for modern AI applications.
Discover why immediate data freshness outperforms strict consistency in real-time decision-making, enabling faster insights and better performance for modern operational systems.
Explore CrateDB Cloud's availability across AWS, Azure, and GCP regions, and learn how your feedback can influence future expansions. Deploy your free cluster today.
Understand the difference between a data historian and a time series database. Compare architectures, use cases, scalability, and when to use each. Learn why modern industrial and IoT teams often combine both — and how CrateDB fits into a unified data stack.
Explore the best time series databases for real time workloads in 2026. Compare InfluxDB, TimescaleDB, ClickHouse, CrateDB, QuestDB, and VictoriaMetrics based on ingestion speed, query performance, scalability, and data model flexibility.