AI in 2026 will move beyond hype toward consolidation, cost efficiency, and real business impact. Explore key predictions shaping the next phase of artificial intelligence.
Real-time data processing enables systems to ingest, analyze, and act on data as it arrives. Learn how it works, key use cases, and how modern architectures support low-latency analytics at scale.
An analytics database enables fast, scalable analysis of large datasets. Learn how analytics databases work, key use cases, and what to look for in modern data architectures.
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.
Analytics needs are evolving. Discover why modern teams rely on analytics databases for real time, operational, and scalable insights.
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.
