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With Postgres 17 officially here, let's talk about how its expanded capabilities are addressing new complex data and AI workloads. Postgres, long known for its transactional strength, is now equipped to handle vector databases and unstructured data, making it a powerful choice for AI-driven applications. This evolution is effectively reshaping how organizations can operationalize AI with Postgres. In this blog, we explore: 🔹 The role of vector databases in powering AI and chatbots 🔹 How RAG (Retrieval Augmented Generation) enhances AI accuracy and intelligence 🔹 Postgres’ ability to manage unstructured data like text, images, and video 🔹 Building a scalable AI data platform with Postgres Check it out here ➡https://bit.ly/3WNFeY1 #EDBPostgresAI #JustSolveItWithPostgres #PG17 #VectorDatabase #RAG #LLMs #UnstructuredData #PostgreSQL

!!!Embracing Vector Databases with Postgres at Quadrant Technologies!!! At Quadrant Technologies, we're continually pushing the boundaries of innovation. By leveraging Postgres with vector extensions (like pgvector), we're seamlessly integrating AI/ML capabilities into our existing solutions. This approach offers us powerful benefits: * Seamless Integration: Embedding AI features without disrupting our SQL workflows. * Scalability & Performance: Efficient similarity searches over large datasets with advanced indexing. * Flexibility: Combining vector and structured data for more holistic data management. * Cost-Effectiveness: Capitalizing on familiar PostgreSQL environments without hefty new investments. By incorporating vector databases into our AI-driven solutions, we’re further enhancing the power of data science and machine learning to drive impactful outcomes. Exciting times ahead! #AI #MachineLearning #PostgreSQL #DataScience #QuadrantTechnologies Vamshi ReddyKaivalya HanswadkarManikyam ThukkapuramPrasanth Venkata Satya TammirajuAbhishek MishraKesava DanduBalaji Raju DigalaBalu KunchamRavi RajuladeviAmarendher AcchaSiva Krishna Varma GajarlaSreeraj VenkitaramananQuadrant Technologies

Hello people, this is my new article called: What about Vector Databases? Be an expert in minutes !!! 🚀🚀. I hope that this works for you. "Vector databases represent a specialized category within database management systems crafted for the storage and retrieval of data in the form of vectors. In this context, a vector serves as a mathematical portrayal of multidimensional data. Unlike traditional databases, vectors in this system go beyond mere data storage; they serve as dynamic entities capturing intricate relationships and similarities between data points. These vectors become not just repositories of information but dynamic instruments that reveal the interconnected web of data relationships and similarities, offering a nuanced perspective in the realm of data management." #data #datadriven #ai #artificialintelligence #faiss #postgresql #milvus #annoy https://lnkd.in/g7VfgCV5

We are excited to unveil EDB's rebrand and introduce our latest product innovation: EDB Postgres AI. This pioneering platform is the industry’s first to integrate intelligence into transactional, analytical, and AI workloads, setting a new standard for #PostgreSQL solutions. EDB Postgres AI is designed to enhance efficiency and drive smarter database management for businesses. Discover more about EDB Postgres AI and how it can elevate your data strategies: www.enterprisedb.com #EDBPostgresAI #JustSolveItWithPostgres #PostgreSQL #AI EDB

Over 35% of enterprises are now considering Postgres for their next data platform, especially when it comes to AI applications. So, how did we get here? What started as an open-source relational database has evolved into the trusted platform of choice for enterprises across industries. Offering both flexibility and power to handle their most complex, mission-critical workloads—now, more than ever, with AI in the mix—Postgres enables organizations to unlock the maximum value of their data, scaling effortlessly while ensuring control and seamless integration across environments. In CDO Magazine, EDB’s Chief Product Engineering Officer Jozef de Vries outlines the four principles for turning your sovereign data and AI into a platform for success with enterprise-grade Postgres. These principles focus on the concrete challenges enterprises face today: centralized observability, secure data access, real-time decision-making, and the ability to handle both structured and unstructured data at scale. To learn how enterprise-grade Postgres is the key to overcoming those challenges and building a future-proof data and AI platform, read Jozef's article here ➡️ https://bit.ly/3ZOnc9Z #EDBPostgresAI #JustUsePostgres #DataInfrastructure #AIPlatform #DataSovereignty #MultimodalData #PostgreSQL17 #JustSolveItWithPostgres

90% of enterprise data is unstructured. Imagine the untapped potential. The majority of that unstructured data is locked away in databases scattered across geographies, domains, and owners—unseen and unused. The financial implications of this are massive. Data-driven organizations are 23x more likely to acquire customers, 6x more likely to retain them, and 19x more likely to be profitable. With that much at stake, the unseen and unused data poses a real problem. Read the new piece from EDB Chief Product Engineering Officer Jozef de Vries in CDO Magazine. It's four simple principles for turning your sovereign data and AI into a platform for success with enterprise-grade Postgres. >> https://lnkd.in/graRw-FT #Postgres #PostgreSQL #justsolveitwithPostgres #EDBPostgresAI #data #AI #database #DBA #opensource

✅ Data: There’s No AI Without It ✅ People: The Most Important Differentiator ✅ Postgres: Leverage open source. Those are the key takeaways you'll discover in the new article in InformationWeek from EDB CEO Kevin Dallas. Follow the link to explore how organizations can harness the power of AI by focusing on data management and building diverse, adaptable teams. After all, AI could contribute a staggering $15.7 trillion to the global economy by 2030. With so much incredible potential available in the emerging intelligent systems economy, business leaders must leverage data and cultivate the right talent to thrive. That's how the enterprise of the future stays competitive in the AI generation. Read the article here: https://bit.ly/3X3t79B #Postgres #PostgreSQL #justsolveitwithPostgres #EDBPostgresAI #data #AI #database #DBA #opensource

Get the buzz on #DataManagement 🚀  https://lnkd.in/ghVkv3fs From Databricks acquiring Tabular to Snowflake open-sourcing Polaris, the industry is witnessing a seismic shift. Cloudera is staying ahead with its embrace of Apache Iceberg and the acquisition of Verta’s AI platform. These moves are setting the stage for a future driven by AI, automation and advanced analytics. “Our decision to make this strategic acquisition isn’t just about growth,” he said. “It’s about delivering an enriched AI experience to our customers and anticipating their needs since the future of data management is AI,” said Venkat Rajaji, SVP of product management at Cloudera. “Data is the foundation for AI and AI applications and where the market is headed.” 👉 Discover how these changes are shaping the future of data management and AI. #CUBEConversations #theCUBE #DataManagement #AI

🚀 Unlock the Power of Connections with Graph Databases 🌐 Graph databases are reshaping how we model and leverage data by making relationships first-class citizens. Unlike traditional databases, they thrive in handling complex, interconnected data, representing it through nodes, edges, and properties—perfect for today’s data-rich world. Why Use Graph Databases? * Schema Flexibility: Evolve data models without rigid schemas. * Efficient Traversals: Quickly navigate multi-hop queries (e.g., "friends of friends"). * Natural Data Representation: Maps real-world relationships, making queries intuitive. Top Graph Databases: 1. Neo4j: Powerful graph operations with Cypher. 2. Amazon Neptune: Managed by AWS, supports RDF and property graphs. 3. OrientDB & ArangoDB: Multi-model, combining graph and document features. 4. TigerGraph: Optimized for deep link analytics. Key Use Cases: * Social Networks: Model user interactions. * Recommendation Engines: Product/content recommendations based on user behavior. * Fraud Detection: Identify unusual patterns among transactions and accounts. * Knowledge Graphs: Build contextual webs of information. * IT Network Management: Understand dependencies and optimize system operations. Graph Query Languages: 1. Cypher (Neo4j) 2. Gremlin (Apache TinkerPop) 3. SPARQL (RDF) The Future of Graph Databases 🌟 Graph databases excel in uncovering hidden connections and answering complex questions at scale, which is critical for AI, ML, and big data analytics. Graph technology is set to play a significant role in AI-driven insights, fraud prevention, and personalization in many industries. Curious about leveraging graph databases in your project? Let’s connect and explore the future of connected data! 💬🚀 #GraphDatabase #Neo4j #AmazonNeptune #NoSQL #DataRelationships #KnowledgeGraph #FraudDetection #TechInnovation #BigData #AI #MachineLearning