EDB Postgres AI continues to evolve as the platform of choice for enterprises pursuing data and AI sovereignty. Our latest release delivers new capabilities that make it easier to build and deploy GenAI agents, modernize legacy databases, and unify visibility across data systems—all within a secure, hybrid environment. From faster migrations and low-code AI pipelines to native support for Model Context Protocol (MCP) tools, these updates give organizations more control over where their data lives, how their models run, and how quickly they scale AI into production. Learn more about the latest EDB Postgres AI features here: https://lnkd.in/eRiGpwBw #EDBPostgresAI #SovereignAI #DataSovereignty #GenAI #AgenticAI #NoCode #LowCode

Traditional databases weren't built for cloud-native AI workloads.🤔 This year’s Distributed SQL Summit keynote, presented by Yugabyte co-founder Karthik Ranganathan, will explore how distributed PostgreSQL enables Kubernetes-first organizations to build ultra-resilient AI applications at enterprise scale.💡 Find out more and register for DSS Day Atlanta on Nov 10th, the most EPIC event at KubeCon!⬇️ https://lnkd.in/dZCWX9Ji Discover how distributed SQL powers GenAI applications, explore real-world use cases, and get first-hand experience with best practices that will help you build ultra-resilient, globally distributed systems at scale.🚀 #distributedsql #RAG #Postgrescompatible #ai #GenAI #database #cloudnative #opensource #yugabytedb #Vector #DSSDay2025 #kubernetes

🚀 Discovering the Data Processing Pipeline in Yandex Cloud for ML Models In the world of machine learning, an efficient data pipeline is key to success. Yandex Cloud has developed an innovative solution that processes terabytes of data daily, optimizing the training of AI models. This approach not only accelerates development but also ensures scalability and reliability in cloud environments. 🔧 System Architecture The core of the pipeline is based on a distributed architecture that integrates components like Apache Kafka for real-time data ingestion, Spark for batch processing, and Kubernetes for orchestration. This allows handling heterogeneous data flows, from user logs to images, with a focus on fault tolerance. • 📊 Ingestion and Storage: Data is captured via streams and stored in S3-compatible storage, ensuring durability. • ⚙️ Transformation: Using DataFlow, ETL jobs are applied to clean and enrich data, reducing preparation time by 40%. • 🧠 ML Training: Integration with TensorFlow and PyTorch, where the pipeline directly feeds GPU clusters for rapid iterations. 💡 Challenges Overcome One of the main challenges was handling massive volumes without latency. Yandex implemented dynamic auto-scaling and monitoring with Prometheus, resolving bottlenecks during load peaks. Additionally, they incorporated security with end-to-end encryption and compliance with regulations like GDPR. This innovation demonstrates how modern clouds can empower AI at an enterprise scale, inspiring teams to adopt similar practices. For more information, visit: https://enigmasecurity.cl #MachineLearning #DataPipeline #YandexCloud #BigData #AI #CloudComputing #TechInnovation If you're passionate about cybersecurity and tech, consider donating to Enigma Security for more content: https://lnkd.in/evtXjJTA Connect with me on LinkedIn to discuss trends in AI and security! https://lnkd.in/e86E98i4 📅 Wed, 01 Oct 2025 07:00:52 GMT 🔗Subscribe to the Membership: https://lnkd.in/eh_rNRyt

🚀 Discovering the Data Processing Pipeline in Yandex Cloud for ML Models In the world of machine learning, an efficient data pipeline is key to success. Yandex Cloud has developed an innovative solution that processes terabytes of data daily, optimizing the training of AI models. This approach not only accelerates development but also ensures scalability and reliability in cloud environments. 🔧 System Architecture The core of the pipeline is based on a distributed architecture that integrates components like Apache Kafka for real-time data ingestion, Spark for batch processing, and Kubernetes for orchestration. This allows handling heterogeneous data flows, from user logs to images, with a focus on fault tolerance. • 📊 Ingestion and Storage: Data is captured via streams and stored in S3-compatible storage, ensuring durability. • ⚙️ Transformation: Using DataFlow, ETL jobs are applied to clean and enrich data, reducing preparation time by 40%. • 🧠 ML Training: Integration with TensorFlow and PyTorch, where the pipeline directly feeds GPU clusters for rapid iterations. 💡 Challenges Overcome One of the main challenges was handling massive volumes without latency. Yandex implemented dynamic auto-scaling and monitoring with Prometheus, resolving bottlenecks during load peaks. Additionally, they incorporated security with end-to-end encryption and compliance with regulations like GDPR. This innovation demonstrates how modern clouds can empower AI at an enterprise scale, inspiring teams to adopt similar practices. For more information, visit: https://enigmasecurity.cl #MachineLearning #DataPipeline #YandexCloud #BigData #AI #CloudComputing #TechInnovation If you're passionate about cybersecurity and tech, consider donating to Enigma Security for more content: https://lnkd.in/er_qUAQh Connect with me on LinkedIn to discuss trends in AI and security! https://lnkd.in/eFb3bY4C 📅 Wed, 01 Oct 2025 07:00:52 GMT 🔗Subscribe to the Membership: https://lnkd.in/eh_rNRyt

RAG AI Agent with Supabase Memory & Google Gemini Integration Developed a Retrieval-Augmented Generation (RAG) agent combining Google Gemini, Supabase Vector Store, and PostgreSQL memory for context-aware conversations—Integrated Google Drive for automatic document embedding, enabling dynamic knowledge retrieval and intelligent responses. #AI #RAG #GoogleGemini #Supabase #VectorDatabase #PostgreSQL #LLM #AIIntegration #DataEngineering #MachineLearning #AIAgent #GenAI

"It’s exciting to see the power of combining distributed PostgreSQL with AI. We look forward to sharing more agentic AI developments and facilitating thoughtful discussions on progress and future possibilities at DSS 2025.”💬 Our latest press release details why Distributed SQL Summit (DSS) Day Atlanta on Nov 10th (co-located at KubeCon) is the place to be! 👀 This seventh annual event will focus on the progress and possibilities of “Distributed PostgreSQL in the AI Era.”🔥 Attendees will benefit from discussions on real-world challenges, such as: ✔️ How to tame database sprawl with multimodal databases ✔️ How to migrate legacy databases to distributed databases ✔️ What to consider when selecting a database for modern and GenAI apps Read the full press release: https://lnkd.in/dzwDkv9m Find out more about DSS Day Atlanta and register⬇️ https://lnkd.in/d2TrkcX3 #distributedsql #RAG #Postgrescompatible #ai #GenAI #database #cloudnative #yugabytedb #opensource #DSSDay2025 #Vector #KubeCon

Do you need to rebuild everything to adopt AI effectively? I’ve spent the past few months talking to engineering and product leaders across industries and almost all face the same challenge: how to build AI capability without rebuilding their stack. I wrote a short piece with my perspective on how SQL, Postgres, and modern data platforms are quietly evolving into the foundation of real AI infrastructure. 👉 See links in the comments. #AI #DataInfrastructure #DataPlatform #Postgres

MinIO just added Apache Iceberg support to AIStore, which means you can now manage structured tables right inside your object storage. For anyone working with AI & analytics pipelines or relying heavily on upserts, this could simplify things — versioned tables, schema evolution Would love to hear thoughts from anyone who’s tested it or compared performance. #MinIO #ApacheIceberg #AIStore #DataEngineering #Lakehouse #AI https://lnkd.in/dCbCaYpw

𝐖𝐡𝐲 𝐌𝐨𝐧𝐠𝐨𝐃𝐁 𝐚𝐠𝐞𝐧𝐭𝐬 𝐧𝐞𝐞𝐝 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐫𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐭𝐡𝐚𝐧 𝐒𝐐𝐋 𝐚𝐠𝐞𝐧𝐭𝐬 🚀 As AI agents become central to product strategy, the data stack must support adaptable reasoning. MongoDB’s flexible documents enable agents to interpret unstructured data and evolving contexts in real time. MongoDB agents weave together chat logs, sensor streams, and user profiles without strict schemas. They surface relevant patterns, tailor prompts, and act on insights without costly schema migrations. Outcome: faster data onboarding, richer context, and more proactive automation. SQL agents excel at fixed transactions, but MongoDB agents thrive on probabilistic reasoning and cross-collection queries. They plan across evolving data, reduce latency by localizing reasoning, and scale with demand. This enables more resilient, context-aware automation across products. From governance to experimentation, MongoDB-based agents provide traceable decisions. Data provenance stays intact as agents reason with flexible schemas, enabling safer experimentation and faster iteration. I’d love to hear how you’re seeing MongoDB agents unlock new reasoning patterns in your organization. #ArtificialIntelligence #MachineLearning #GenerativeAI #AIAgents #MindzKonnected

🚀 Introducing the RAG Agent [MongoDB] Workflow! Created by Vraj Golakiya, this intelligent n8n automation combines the power of Groq LLM, MongoDB Atlas, Hugging Face, and Google Drive to build a fully automated AI chat system 🧠💬 The workflow auto-imports PDFs from Google Drive, transforms them into embeddings, stores them in MongoDB, and enables real-time chat with an AI agent that retrieves precise answers — directly from your documents! ⚡ This project showcases the perfect fusion of AI, automation, and data intelligence. 💡 🎥 Watch the full workflow demo below 👇 #n8n #MongoDB #RAG #LangChain #Automation #AI #Groq #HuggingFace #TechInnovation #AIAutomation #YBAISolution #AIWorkflow #IntelligentAutomation