CrateDB

Weekly Digest – May 7–14 📺 Recording | Why Industrial IoT Data Breaks Traditional Databases — and What to Do About It 📈 Explore | See CrateDB in Action

⏱️ “Real-time” isn’t real if you can’t query fresh data instantly. In many setups, data is ingested in one place… …and only becomes available for queries much later. That delay? It kills operational decisions. CrateDB removes this gap entirely: Ingest → query → insight in milliseconds 👉Watch our blueprint on how you can solve this issue: https://hubs.ly/Q04dwd3D0 #Analytics #RealTime #Data #RealTimeAnalytics #BigData

When a storm is forecast near Newark Airport, United Airlines already knows what to do. -> their systems automatically run every possible flight permutation and adjust plans in real time to protect passengers, crew, and aircraft. What used to be hours of manual analysis now happens in seconds. This is what MIT researchers call a "real-time business", that come with remarkable metrics compared to the rest: → 62% higher revenue growth → 97% higher profit margins → 20% better at innovation What separates them isn't just technology. It's a different relationship with data and decision-making. Most companies are somewhere in the middle of this journey. The ones moving fastest aren't necessarily the biggest. They're the ones who decided that waiting is no longer an option. Where does your organization sit on that spectrum? #RealTimeBusiness #DigitalTransformation #BusinessAgility #Leadership #DataDriven

2,200 TB per month from a single automotive plant & most of it never gets queried in time to matter. Data integration is a huge part of the challenge. In our experience, another bottleneck often shows up right after: even when data flows across silos, the query layer can struggle with the volume and variety at the speed decisions require. The digital thread concept is spot on and it raises the bar for what the underlying data infrastructure needs to handle. Curious what others are seeing: where does it break down in practice?

Industrial IoT data isn’t just growing — it’s getting harder to control. More sensors. More formats. More decisions that can’t wait for a batch window. And most databases, built for a different era, are starting to show their limits. Join us for this session, where Dr. Johannes Held (Principal at Dataciders GmbH) joins Gregor Bauer (VP Customer Engineering at CrateDB) for a candid discussion on what actually breaks in production — and how to fix it. Registration: https://hubs.ly/Q04djL0b0 They cover: ➡️ Why classic architectures fail at data volume and variety — and what the failure looks like in practice ➡️ How CrateDB handles high-cardinality time-series data without pre-aggregation or proprietary query languages ➡️ What drop-in PostgreSQL compatibility means for real-world adoption speed ➡️ How to keep a database that scales as requirements change — without a migration project every three years ➡️ Data sovereignty and European deployment considerations for regulated industrial environments A practical, technical exchange between two people solving these problems every day.

What an amazing time at the Data Innovation Summit in Stockholm! 🙌 🇸🇪 A big thank you to everyone who joined Gregor’s talk, stopped by the CrateDB booth or participated in our workshop & demos. The conversations around data, IoT, and real-time analytics were truly inspiring! 🚀 If you didn’t get a chance to connect with us, feel free to reach out. We’d love to continue the discussion! #DIS2026x1 #RealTimeAnalytics #TechCommunity

Weekly Digest – April 30–May 7 📺 Recording | Architecting Real-Time Data Pipelines: Turning Sensor Streams into Actionable Dashboards 💻 Webinar | Why Industrial IoT Data Breaks Traditional Databases — and What to Do About It 📊 Explore | Hands-on CrateDB for Different Scenarios

What happens when you combine full-text and vector search in a single SQL query? 👉 You get search that understands context; not just keywords. In our new guided experience with CrateDB, you can try it yourself: • perform full-text search using SQL • work with vector embeddings and similarity search • combine lexical and semantic search in one query • build search that understands context, not just keywords 💡 Spin up CrateDB, choose the Hybrid Search scenario, import real data from the City of Chicago, and run queries that go beyond simple keyword matching: https://hubs.ly/Q04dwcDf0

Looking forward to Day 2 at the Data Innovation Summit 2026 🚀 After a great start today, the CrateDB team is ready for another day full of conversations around real-time data, AI, and analytics. If you’re attending tomorrow, make sure to catch this session: 📍 Stand C65 🕥 May 7, 10:15-10:45 AM 🎤 Unstoppable Insights: Resilient Data Streaming with CrateDB Stop by the booth anytime and say hello 👋

One thing we hear constantly from engineering teams: ingestion works fine in isolation. Queries work fine in isolation. But the moment you run a complex query on live data, performance collapses. The two workloads fight each other. Mixed workloads are where most real-time architectures struggle. Most architectures force a choice: fast ingestion or fast queries. CrateDB doesn't. 🎥 More on how to solve this challenge in our webinar recording: https://hubs.ly/Q04d1Nch0 #RealTimeData #DataEngineering #DataArchitecture #CrateDB