A first look at TypeDB 3 benchmarks, showing great performance at scale, real-world OLTP workloads, and comparisons to previous versions, and Neo4j.

An open-source Text2SQL tool that transforms natural language into SQL using graph-powered schema understanding. Ask your database questions in plain English, QueryWeaver handles the weaving. - Fal...

DuckDB community extension that adds support for SQL/PGQ and graph algorithms

Explore an iterative verification and correction refinement process aimed at improving Text2Cypher performance.

Graph database implementing the RDF and SPARQL standards. Very fast and scales to hundreds of billions of triples on a single commodity machine. - ad-freiburg/qlever

When we present QLever, people often ask "how is this possible" as our speed and scale is on another dimension. We now have a page in the wiki that goes into a bit more detail on why and how this is possible. In short: • Purpose built for large scale graph data, not retrofitted • Indexing optimized for fast queries without full in-memory loading • Designed in C++ for efficiency and low overhead • Integrated full text and spatial search in the same engine • Fast interactive queries even on hundreds of billions of triples Link to the wiki page in the comments.

Qlever: graph database implementing the RDF and SPARQL standards. Very fast and scales to hundreds of billions of triples on a single commodity machine. Sounds to good to be true, anyone tested this out? https://lnkd.in/esXKt79J #GraphDatbase #ontology #RDF | 14 comments on LinkedIn

Discover how LMPG transforms graph databases by treating properties as first-class citizens rather than simple node attributes. This comprehensive technical guide explores RushDB's groundbreaking architecture that enables automatic schema evolution, property-first queries, and cross-domain analytics impossible in traditional property graphs or RDF systems.

: Users left wondering whether to fork it or forget it as another FOSS project bites the dust

It's with deep gratitude for the amazing product the #KuzuDB team created, and a mix of necessity and excitement, that I announce the launch of Ladybug. This is a new open-source project and a community-driven fork of the popular embedded graph database.

Kuzu is no more The project was archived last night with one last major release. The communication has not been very clear, but I can bet Semih Salihoğlu is under a lot of pressure and I am looking forward to hearing the full story someday. We liked the product and will fork it and continue supporting it as a way for our users to run local memory workloads on their machines. We'll not support it in production anymore though, since we are not database developers and don't plan to be. You can only get that far without the need to grow a mighty Unix beard. Instead, we'll be going with Neo4j for larger loads and our partner Qdrant for embeddings + extend our FalkorDB and Postgres support. It does feel a bit strange when your default DB disappears overnight. That is why cognee is database agnostic and all features that were Kuzu specific will be migrated in about 2 weeks. This time we were just too fast for our own good. | 47 comments on LinkedIn

Last week, the Kùzu Inc team announced that they will no longer actively support the open-source KuzuDB project. I've been a fan of KuzuDB and think its discontinuation leaves a big gap in the graph ecosystem. This is especially the case for open-source solutions – over the last few years, many open-source graph database systems were forked, relicensed or discontinued. Currently, users looking for an OSS graph database are left to pick from: - community editions of systems with enterprise/cloud offerings (Neo4j, Dgraph) - variants of a heavily-forked system (ArcadeDB / YouTrackDB, HugeGraph) - projects under non-OSI approved licenses - experimental systems (e.g., DuckPGQ) I'm wondering whether this trends continues or someone steps up to maintain KuzuDB or create a new OSS system.

For years, I considered graph databases “interesting but niche.” Relevant commercially for social networks, supply chain and academically for biotech, maybe some knowledge management. Basically, not something most companies would ever need. I stand corrected. With AI, they’re having a very big moment! Working with graphs the first time feels unusual but also just right. The best analogy I have is that feeling we get when we try to visualize a higher dimension when all we have ever known are three (+ time for the purists). (or is it just me?) Two use-cases that I have been riffing on: * Knowledge management: For me it started as a personal project for personal knowledge management. For enterprises, this is where RAG shines. But I also wonder if there are other applications within Enterprise Knowledge Management that we aren’t thinking of yet.  * Master Data Management (MDM): Potentially a subset of above, but explicitly about attributes and relationships that columnar databases might handle too rigidly. I am a lifetime subscriber for relational and SQL till they exist. Not saying they will go away. Graphs still feel intuitive and unusual at the same time. They are still complex to build (although companies like Neo4j simplify them really well), and difficult to traverse / interpret. I believe there is a stronger convergence of these 2 systems coming. Graphs will augment relational before replacing in some of these use-cases. But they have to be way more simplified first for greater adoption. Would love to hear more from graph experts and/or from those who share this feeling of “just right” for graphs. Are you seeing use-cases where graph databases are picking up? #AI #DataStrategy #Graphs #KnowledgeManagement #MDM | 37 comments on LinkedIn

Let's chat a bit about the use of graph databases in retrieval-augmented generation (RAG). One problem in GenAI is that while the LLMs are fed a lot of text during training, perhaps a model isn't fed the specific information the user is asking about, which could be in a private corporate document. Since the dawn of GenAI, pipelines have existed to store private documents in a vector database and search for text relevant to the user's question in the database. This text is then fed to the LLM for use in generating the answer to the user query. One problem in such pipelines is that the document search may retrieve a lot of text containing terms similar to those in the user query which still isn't relevant to answering the query. At this point, many folks say, "knowledge graphs to the rescue!" Knowledge graphs after all can store information about entities mentioned in private documents, so can't they help disambiguate user questions? Graph DBs have been used in RAG for some time now; I started with them in 2021, before ChatGPT existed. There are various problems with using graph data in RAG. First off, the knowledge graphs we are trying to leverage are themselves generated by machine learning. But what are the guarantees that ML engineers are training their models or agents to produce useful KGs? Are we even using the right kind of statistical learning, never mind agent architectures? After all, if you are going to build a KG based on information in natural language, then you are parsing out conceptual relations from natural language, which are dependent on syntax. So perhaps we should be utilizing machine learning in the syntactic parsing problem, so that we ensure a relation isn't added to the graph if the syntax expresses the negation of the relation, for instance. To graph data modelers, again I maintain that methods for extracting information from syntax have more bearing on the use of graph data in RAG than existing modeling techniques that fail to factor in natural language syntax just like most ML inference fails here. And perhaps graph databases aren't even the right target for storing extracted conceptual relations; I switched to logic databases after a month of working with graphs. The use of KGs and logic bases in RAG needs to be tackled through innovations in syntax parsing like semantic grammars, and through better techniques for performant inference engines than graph query, such as GPU-native parallel inference engines. This isn't a problem I expect to be solved through Kaggle competitions or corporate R&D leveraging recently minted ML engineers.

A sophisticated knowledge graph memory system that stores interconnected information with rich semantic structure using Neo4j. - shuruheel/mcp-neo4j-shan

Why TuringDB choose and managed to create a columnar graph database?

I’ve tested countless Retrieval-Augmented Generation (RAG) systems, and they all failed to meet my expectations. I found a simple two-step…

Knowledge graphs is a powerful concept where we can represent any flow or entity relationships in a structured way.

𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚 𝗼𝗿 𝗥𝗔𝗚 is now flexing to the max using LlamaIndex, supports 𝟳 𝗴𝗿𝗮𝗽𝗵 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀, 𝟭𝟬 𝘃𝗲𝗰𝘁𝗼𝗿 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀, 𝟭𝟯 𝗱𝗮𝘁𝗮 𝘀𝗼𝘂𝗿𝗰𝗲𝘀, 𝗟𝗟𝗠𝘀, Docling 𝗱𝗼𝗰 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴, 𝗮𝘂𝘁𝗼 𝗰𝗿𝗲𝗮𝘁𝗲 𝗞𝗚𝘀, 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚, 𝗛𝘆𝗯𝗿𝗶𝗱 𝗦𝗲𝗮𝗿𝗰𝗵, 𝗔𝗜 𝗖𝗵𝗮𝘁 (shown Hyland products web page data src) 𝗔𝗽𝗮𝗰𝗵𝗲 𝟮.𝟬 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 𝗚𝗿𝗮𝗽𝗵: Neo4j ArcadeDB FalkorDB Kuzu NebulaGraph, powered by Vesoft (coming Memgraph and 𝗔𝗺𝗮𝘇𝗼𝗻 𝗡𝗲𝗽𝘁𝘂𝗻𝗲) 𝗩𝗲𝗰𝘁𝗼𝗿: Qdrant, Elastic, OpenSearch Project, Neo4j 𝘃𝗲𝗰𝘁𝗼𝗿, Milvus, created by Zilliz (coming Weaviate, Chroma, Pinecone, 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝗦𝗤𝗟 + 𝗽𝗴𝘃𝗲𝗰𝘁𝗼𝗿, LanceDB) Docling 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗦𝗼𝘂𝗿𝗰𝗲𝘀: using LlamaIndex readers: working: Web Pages, Wikipedia, Youtube, untested: Google Drive, Msft OneDrive, S3, Azure Blob, GCS, Box, SharePoint, previous: filesystem, Alfresco, CMIS. 𝗟𝗟𝗠𝘀: 𝗟𝗹𝗮𝗺𝗮𝗜𝗻𝗱𝗲𝘅 𝗟𝗟𝗠𝘀 (OpenAI, Ollama, Claude, Gemini, etc.) 𝗥𝗲𝗮𝗰𝘁, 𝗩𝘂𝗲, 𝗔𝗻𝗴𝘂𝗹𝗮𝗿 𝗨𝗜𝘀, 𝗠𝗖𝗣 𝘀𝗲𝗿𝘃𝗲𝗿, 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 𝘀𝗲𝗿𝘃𝗲𝗿 𝗚𝗶𝘁𝗛𝘂𝗯 𝘀𝘁𝗲𝘃𝗲𝗿𝗲𝗶𝗻𝗲𝗿/𝗳𝗹𝗲𝘅𝗶𝗯𝗹𝗲-𝗴𝗿𝗮𝗽𝗵𝗿𝗮𝗴: https://lnkd.in/eUEeF2cN 𝗫.𝗰𝗼𝗺 𝗣𝗼𝘀𝘁 𝗼𝗻 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚 𝗼𝗿 𝗥𝗔𝗚 𝗺𝗮𝘅 𝗳𝗹𝗲𝘅𝗶𝗻𝗴 https://lnkd.in/gHpTupAr 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰𝘀 𝗕𝗹𝗼𝗴: https://lnkd.in/ehpjTV7d

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I am excited to announce the formation of a Data Façades W3C Community Group. Façade-X, initially introduced at SEMANTICS 2021 and successfully implemented by the SPARQL Anything project, provides a simple yet powerful, homogeneous view over diverse and heterogeneous data sources (e.g., CSV, JSON, XML, and many others). With the recent v1.0.0 release of SPARQL Anything, the time was right to work on the long-term stability and widespread adoption of this approach by developing an open, vendor-neutral technology. The Façade-X concept was born to allow SPARQL users to query data in any structured format in plain SPARQL. Therefore, the choice of a W3C community group to lead efforts on specifications is just natural. Specifications will enhance its reliability, foster innovation, and encourage various vendors and projects—including graph database developers — to provide their own compatible implementations. The primary goals of the Data Façades Community Group is to: Define the core specification of the Façade-X method. Define Standard Mappings: Formalize the required mappings and profiles for connecting Façade-X to common data formats. Define the specification of the query dialect: Provide a reference for the SPARQL dialect, configuration conventions (like SERVICE IRIs), and the functions/magic properties used. Establish Governance: Create a monitored, robust process for adding support for new data formats. Foster Collaboration: Build connections with relevant W3C groups (e.g., RDF & SPARQL, Data Shapes) and encourage involvement from developers, businesses, and adopters. Join us! With Luigi Asprino Ivo Velitchkov Justin Dowdy Paul Mulholland Andy Seaborne Ryan Shaw ... CG: https://lnkd.in/eSxuqsvn Github: https://lnkd.in/dkHGT8N3 SPARQL Anything #RDF #SPARQL #W3C #FX

Many teams adopt graph databases believing they need specialized tools for relationship data, adding unnecessary complexity to their stack. This session reveals that for most use cases, the performance benefits don't justify the overhead. You'll learn to evaluate whether you truly need graph DB capabilities and how to implement graph patterns using simpler alternatives.

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The Linked Data Benchmark Council has, for over a decade, been the quiet force behind much of the progress in graph technology. Their mission is deceptively simple: to design, maintain, and promote standard benchmarks for graph data management systems. Read about the recent meeting and the announcem

Introducing Brahmand: a Graph Database built on top of ClickHouse. Extending ClickHouse with native graph modeling and OpenCypher, merging OLAP speed with graph analysis. While it’s still in early development, it’s been fun writing my own Cypher parser, query planner with logical plan, analyzer, and optimizer in Rust. On the roadmap: native JSON support, bolt protocol, missing Cypher features like WITH, EXISTS, and variable-length relationship matches, along with bitmap-based optimizations and distributed cluster support. Feel free to check out the repo: https://lnkd.in/d-Bhh-qD I’d really appreciate a ⭐ if you find it useful!

Ever caught yourself bouncing between your code editor and database client just to test a single query? Annoying, right? That context switching kills your flow. Now you can bring the Ultipa VS Code Extensions for ISO GQL to your workflow! Write, validate, and execute ISO GQL queries right where you

Executive Summary While both Vector Databases and Graph Databases are pivotal technologies in modern artificial intelligence (AI) and data management, they operate on fundamentally different principles of data organization and retrieval. Vector Databases manage data based on statistical similarity w

The Neo4j Infinigraph uses property sharding to distribute properties across multiple database shards while keeping the graph structure intact.

Meet Infinigraph, a scalable, distributed graph architecture that allows you to run 100TB+ operational and analytical graph workloads in a single system.

Calling all data scientists, developers, and managers! 📢 Looking to level up your team's knowledge of graph technology? We're excited to share the recorded 2-part training series, "Graph Tech Demystified" with the amazing Paco Nathan. This is your chance to get up to speed on graph fundamentals: In Part 1: Intro to Graph Technologies, you'll learn: - Core concepts in graph tech. - Common pitfalls and what graph technology won't solve. - Focus of graph analytics and measuring quality. 🎥 Recording https://lnkd.in/gCtCCZH5 📖 Slides https://lnkd.in/gbCnUjQN In Part 2: Advanced Topics in Graph Technologies, we explore: - Sophisticated graph patterns like motifs and probabilistic subgraphs. - Intersection of Graph Neural Networks (GNNs) and Reinforcement Learning. - Multi-agent systems and Graph RAG. 🎥 Recording https://lnkd.in/g_5B8nNC 📖 Slides https://lnkd.in/g6iMbJ_Z Insider tip: The resources alone are enough to keep you busy far longer the time it takes to watch the training!