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.
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.
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.
Discover how CrateDB’s real-time query engine delivers instant insights with powerful capabilities like aggregations, ad-hoc queries, hybrid search, and AI features, all using standard SQL.
A time series database manages data indexed by time, enabling fast analysis of streaming data. Discover how CrateDB combines SQL simplicity, unlimited cardinality, and context enrichment to deliver real-time insights at scale.
Learn how columnar databases optimize analytical performance and how CrateDB combines columnar speed with real-time ingestion and PostgreSQL simplicity.
Discover how modern SaaS platforms leverage real-time analytics to enhance user experience, optimize operations, and gain a competitive edge.
Discover how CrateDB enables real-time analytics and AI at the edge, enhancing operational efficiency and reducing costs across various industries.
Discover how CrateDB’s Lucene-based storage optimizes real-time analytics, enabling efficient, scalable, and adaptive data management for continuous insights.
Discover how moving beyond traditional data warehouses and lakes to real-time databases like CrateDB can revolutionize decision-making and operational efficiency.
