The NoSQL comeback nobody saw coming
Graceful database schema evolution: the PRISM workbench: Proceedings of the VLDB Endowment: Vol 1, No 1
Supporting graceful schema evolution represents an unsolved problem for traditional information systems that is further exacerbated in web information systems, such as Wikipedia and public scientific databases: in these projects based on multiparty ...
Keynote: The Death of the Browser - Rachel-Lee Nabors, AgentQL
Designing Data-Intensive Applications (DDIA) — an O’Reilly book by Martin Kleppmann (The Wild Boar Book)
NoSQL… Big Data… Scalability… CAP Theorem… Eventual Consistency… Sharding…
Semantic Data Modeling, Graph Query, and SQL, Together at Last?
Our teams advance the state of the art through research, systems engineering, and collaboration across Google.
#opensource | Andre Zayarni | 61 comments
We have a long history with MongoDB. Many years ago, when I was still writing code myself, NoSQL was a novel concept. I was sold on its simplicity and natural compatibility with the Node stack. I used Mongo for all my side projects. For work, we still preferred SQL DBs, though. Fast forward to the early days of Qdrant, MongoDB Ventures reached out, and we had several conversations; they considered investing in our seed round. Their product managers and engineers were reaching out and praising our solution. At some point, they revealed that they are going to add vectors to their Atlas search product just by using Lucene. So what, who cares? At Qdrant, we’re not retrofitting anything. We’re building a dedicated vector search engine - from the ground up. If someone wants to replicate what we do? Go ahead, we are #opensource. But here’s where it gets frustrating. We have put in the hard work building our community and attracting prominent people in the AI landscape to use our product and create content about it. We have built our Qdrant Stars program to engage with the community even closely. qdrant.tech/stars So, what does it have to do with Mongo? Well, they just made a clever shortcut and reached out to our Qdrant Stars program members and influencers from our community, making them an offer they could not decline. I do not blame the content creators, it is their daily work and it is an excellent opportunity for them, guys, I can totally understand you, no worries. But let's call it what it is: A multi-billion-dollar company is now trying to buy its way into a community built by an early-stage startup, not them. We don’t fear competition. But when the incumbents start copying your strategy and poaching your community, it says one thing loud and clear: We’re making the right people nervous. We’ll never outspend Mongo, and we don’t need to. We have something they can’t copy or buy: a fast-growing, mission-driven, technically superior open platform - and a community that sees through the noise. So to the engineers, researchers, and creators who believe in open innovation, performance that doesn’t compromise, and communities that are earned, not acquired: you know where to find us. And to the MongoDB marketing geniuses: take on Elastic, take on Redis, take on Oracle, and others of your calibre, but leave Qdrant and other startups like us alone. | 61 comments on LinkedIn
A Graph-Based Firebase
This essay covers the design behind Instant. If the schleps we face as UI engineers are actually database problems in disguise, would a database-looking solution solve them?
Relational foundations for functorial data migration | Proceedings of the 15th Symposium on Database Programming Languages
In this paper we present a simple database definition language: that of categories and functors. A database schema is a small category and an instance is a set-valued functor on it. We show that morphisms of schemas induce three ''data migration ...
Composing schema mappings: Second-order dependencies to the rescue: ACM Transactions on Database Systems: Vol 30, No 4
A schema mapping is a specification that describes how data structured under one schema (the source schema) is to be transformed into data structured under a different schema (the target schema). A fundamental problem is composing schema mappings: given ...
Philipp Schmid on Twitter / X
Should we build the web for agents, not agents for the web? 🤔 A new paper argues that current research is misguidedly focuses on improving LLMs leading to significant problems with efficiency, reliability, and safety, proposing a new "Agentic Web Interface" (AWI) that sits on… pic.twitter.com/I90k6kdYdk— Philipp Schmid (@_philschmid) June 14, 2025
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora
In the current era of information abundance, transforming vast amounts of unstructured data into structured, machine-readable knowledge remains one of the most significant challenges in artificial intelligence. Knowledge Graphs (KGs) have emerged as the cornerstone technology for this transformation Zhao et al. (2024), providing the semantic backbone for applications ranging from search engines and question answering Wu et al. (2024); Chen et al. (2024c); Zong et al. (2024); Sun et al. (2024b) to recommendation systems Lyu et al. (2024) and complex reasoning tasks Li et al. (2024b). Yet despite their critical importance, current KG construction approaches remain hampered by an inherent paradox: they require predefined schemas created by domain experts, which fundamentally limits their scalability, adaptability, and domain coverage.
IWE - Agent Memory in Plain Markdown
A local-first knowledge graph for you and your AI agents. Query markdown like a database, edit it with guarded operations, enforce structure with schemas.
surrealdb/surrealdb
A scalable, distributed, collaborative, document-graph database, for the realtime web
The Meshwork of Relational Infrastructure
What makes up Relational infrastructure when we zoom in? What are the qualities and attributes that might help us explain how we build it, nurture it and best invest in it - and indeed, what we can expect in return?
Supporting Our AI Overlords: Redesigning Data Systems to be Agent-First
Large Language Model (LLM) agents, acting on their users' behalf to manipulate and analyze data, are likely to become the dominant workload for data systems in the future. When working with data,...
