Virtuoso Open Source DBMS Now Live

💥 Boom 💥 A new release of Virtuoso is out—our secure, high-performance, and scalable platform for managing data spaces: databases, knowledge bases, filesystems, and APIs—now loosely coupled with AI agents and skills. In the age of AI agents and agent skills, Virtuoso is a natural and effective complement. Much like the original vision of the Semantic Web, it benefits from natural language as a universal interface—while staying firmly grounded in open standards, ensuring agility without the compounding technical debt of silos. Think SQLite or PostgreSQL—but extended. Seriously extended. Alongside native SQL, Virtuoso supports SPARQL, GraphQL, GQL, and openCypher. It also speaks HTTP and WebDAV natively, turning hyperlinks into true super-keys—enabling data access by reference that goes far beyond traditional primary and foreign keys. The result is simple but powerful: Web-like connectivity (private or public) becomes dramatically more expressive when loosely coupled with your data spaces—databases, knowledge bases, filesystems, and APIs. And best of all, it’s easy to install and use. Enjoy. #CDO #CDIO #CAIO #CIO #CTO #CMO #CISO #CxO #AI #Agentic #AgenticWeb #SemanticWeb #SemanticLayer #KnowledgeGraphs #ContextGraph #Ontology #LinkedData #SQL #SPARQL #GQL #openCypher #GrapQL #DBMS #DataSpaces #Database

ORMs solve 90% of database work. The other 10% is where engineering happens For most CRUD, the ORM lets me move fast. But for complex aggregations, heavy reporting, & massive joins, I break the glass & write raw SQL You don't outgrow the ORM. You just outgrow the illusion that any abstraction is perfect #SoftwareEngineering #database #ORM #BuildInPublic

Finally — the new AI Assistant + Analytics features are now implemented in SQL Planner and RELEASED 🚀 You can download SQL Planner from the official website here: https://lnkd.in/g9MYzUfx 🎥 Demo Video: https://lnkd.in/guxj_Eng More AI-powered implementations are already in progress and will be released within the next week. Take a look at the demo and share your feedback — I’d love to know what features you’d want next! Most database monitoring tools only tell you that something is broken. 🛑 I wanted to build an assistant that actually helps you fix it — in plain English. That’s why I added a brand-new AI Assistant feature to SQL Planner to remove the manual frustration of database troubleshooting. Without AI: When performance drops or alerts spike, developers and IT admins often need to: • Dig through DMVs • Search query hashes • Analyze execution details • Spend hours identifying the real issue With SQL Planner AI: The assistant works alongside your monitoring data and instantly provides: ✅ Human-readable explanations ✅ Root-cause insights ✅ Suggested next steps ✅ Performance analysis in plain language You can ask questions like: 👉 “What are my top wait types right now and what do they mean?” 👉 “Is any query blocking happening right now?” 👉 “Which query is consuming the most CPU?” 👉 “How much disk space are my databases using?” 🔒 Enterprise Security First One of the biggest questions I get is: “Does the AI connect directly to my production SQL Server?” Absolutely not. Security and privacy were top priorities while designing this system. SQL Planner connects to your server using the restricted permissions you define. Only safely aggregated monitoring metadata and metrics are processed securely through OpenAI API endpoints. Your live production database remains fully isolated and protected. This is just the beginning — more intelligent analytics and automation features are coming soon. Check out the demo below 👇 I’d genuinely love to hear how features like this could improve your team’s daily debugging and monitoring workflow. #SQL #SQLServer #Monitoring #WindowsServer #Software #AI #DBA #DBMS #Admin

Day 24 & 25 – LinkedIn Challenge Spent time diving deeper into Normalization in DBMS and understanding how to design efficient database structures. Explored how improper table design can lead to redundancy, anomalies, and inconsistency, and how normalization helps solve these issues step by step. Learned about organizing data into multiple related tables, maintaining clear relationships, and ensuring that each piece of data is stored logically. Also focused on concepts like functional dependencies and how breaking down tables into higher normal forms improves data integrity and scalability. This process made me realize how important a strong database foundation is for building reliable and efficient systems. #DBMS #Normalization #DatabaseDesign #SQL #LearningJourney #StudentDeveloper #TechGrowth

At Memgraph, we've been building the fastest in-memory graph database on the planet 🚀 We're introducing Memgraph Zero and its first product: MemGQL - a federated GQL query engine that lets you query any data as a graph, right where it lives. No ETL. No pipelines. No stale snapshots. No moving data. Here's the problem we kept seeing: → Teams spinning up ETL jobs just to ask a question → AI agents bottlenecked by the infrastructure they depend on → Regulated data that can't move - but still needs to be queried together → A dozen databases, none of them talking to each other. MemGQL solves this with one simple idea: connect, don't collect. It implements ISO/IEC 39075 (the new international GQL standard), speaks Bolt protocol out of the box, and connects to: ▸ Memgraph & Neo4j ▸ PostgreSQL & MySQL ▸ DuckDB & Apache Iceberg ▸ ClickHouse & Apache Pinot One query. Every engine. Live data. This is especially powerful for the agentic era - where agents need fresh context across multiple data sources, right now, not from last night's batch job. 🎓 To learn more, join the upcoming Community Call: https://lnkd.in/dY3EZEq8 🛠️ Check out the docs and let us know what you build: https://lnkd.in/dJ9vQ_pZ #GraphDatabase #DataEngineering #GQL #Memgraph #ArtificialIntelligence #DataInfrastructure #OpenSource

L47 First Normal Form (1NF): The foundational step in database normalization. A relational database schema satisfies 1NF when it strictly enforces attribute atomicity and the elimination of repeating groups. Atomicity dictates that every column must contain only indivisible, single values. Multivalued attributes—such as storing comma-separated values within a single field—are strictly prohibited. Consider a table containing `ID`, `PersonName`, and `Order`, where a single order cell contains "Muffin, Sugar". This structure violates 1NF because the data is not atomic. Common ways to handle this include: 1. Row Duplication: Create a distinct row for every item in the multivalued attribute. This requires establishing a composite Primary Key (e.g., `ID` + `Order`) to maintain row uniqueness. 2. Column Expansion: Introduce separate, static columns for each possible value (e.g., `Order1`, `Order2`). This approach is generally discouraged as it limits flexibility and scalability. 3. Table Decomposition (Best Practice): Divide the original structure into a primary base table (e.g., `Person`) and a related referencing table (e.g., `Person_Orders`), linking them relationally. Enforcing 1NF eliminates hidden data structures and serves as the foundation for higher normal forms. #DBMS #SQL #Databases

Meet DuckDB — Born in a Research Lab, Built to Beat Enterprise Tools A tiny database from a Dutch research lab is quietly making analysts rethink everything. DuckDB was created at CWI Amsterdam — the same institute that pioneered columnar databases decades ago. No VC funding. No hype machine. Just two researchers who wanted faster, simpler analytics. And it delivers. Here's what it does differently: No server. No setup. No cloud bill. Just point it at your CSV or Parquet file — and run SQL. That's it. → Query a 50GB file on an 8GB laptop → Process 1TB in 30 seconds → Replaces pandas, Spark & Airflow for 80% of use cases → Free. Open source. Runs everywhere. Why does this matter for analysts? Most of us have the same story — export from the ERP, open in Excel, Excel chokes, switch to Python, spend 30 mins on pandas. Rinse. Repeat. DuckDB cuts that loop entirely. You write SQL. You get answers. Fast. I work with TechnologyOne EAM exports daily — large CSVs, no direct API. DuckDB sits right between that raw data and my Power BI dashboards. No infrastructure. No overhead. Just results. This is the SQLite moment for analytics. 🔗 Try it free → duckdb.org #DuckDB #DataAnalytics #SQL #OpenSource #PowerBI #DataEngineering #LocalGovernment

Your #SQL query probably does NOT execute in the order you think it does. Even though we WRITE queries as: SELECT → FROM → WHERE → ORDER BY the database internally processes them very differently. That realization changed how I think about: • indexing • joins • filtering • sorting • query optimization Because at scale, #SQL performance is often less about syntax and more about: how much data the database is forced to move internally. The deeper I study backend systems, the more I realize: many scalability problems are really unnecessary data movement problems. #SQL #Databases #BackendEngineering #SystemDesign #SoftwareEngineering

🚀 Ready to Master SQL? Here is the Ultimate Roadmap! 🌳📊 Whether you're just starting out or looking to level up your database skills, I've put together the complete SQL Master Tree. It covers everything you need to go from beginner to pro! Here is a quick breakdown of what’s inside: 🔹 The Basics: DB Fundamentals, Data Types & Operators 🔹 Core Commands: DDL, DML, & DQL (Mastering the SELECT statement!) 🔹 Leveling Up: Joins, Subqueries, Views, & Indexing 🔹 Pro Mode: Advanced SQL (CTEs, Window Functions), ACID Properties, & Performance Optimization 🔹 Real-World Impact: Backend APIs, ETL Pipelines, & Data Warehousing Save this roadmap to guide your learning journey! 📌 What is your favorite SQL feature or the one you find most challenging? Let me know in the comments! 👇 #SQL #DataAnalytics #DataScience #Database #BackendDevelopment #TechRoadmap #LearnCoding #DeveloperCommunity #Viral

Time-series workloads expose the limits of traditional relational SQL surprisingly quickly. Some operations become extremely verbose. Others are difficult for the optimizer to execute efficiently. And some time-aware patterns simply do not map naturally onto generic relational semantics at all. That is why #QuestDB extends SQL in areas where time-series workloads need different primitives: * LATEST ON * SAMPLE BY * time-aware joins * designated timestamps * temporal filtering syntax * arrays for structures like order books We published a new docs page covering these SQL extensions, what they do, and where they fit naturally in time-series workloads. Link in first comment 👇