Interesting academic paper on how data platforms and architectures should be redesigned to be #agentic-first (or agent-first). This requires fundamental changes to how data is served by data platforms including new query interfaces, new query processing techniques, and new agentic memory stores. Serving Our AI Overlords: Redesigning Data Systems for Agents https://lnkd.in/gHNiT_PE #AI #agentic

How do you integrate AI LLMs into Data Governance? Well, a little over a year ago, I worked on a project where the goal was simple: reduce costs and improve governance in Snowflake. We approached it in two phases: 🔹 Phase 1 – Traditional Approach Used metadata tables to identify inactive users and disable them. Flagged stale datasets not queried in months and moved them to cheaper storage or purged them. Manual scripts + scheduled tasks got us a solid 20–30% cost reduction and tighter security. 🔹 Phase 2 – Early AI/LLM Adoption Leveraged Snowflake’s Cortex AI functions (AI_CLASSIFY, AI_COMPLETE, etc.) to analyze usage logs + object metadata. LLMs helped classify tables into active, archival, or purge candidates based on usage patterns and documentation. Built AI-driven alerts for inactive users, idle warehouses, and redundant datasets. This second layer brought additional 15–20% savings and far less manual review. Lesson learned: start with the basics, then layer in AI. The real magic comes when LLMs work alongside traditional governance—faster, smarter, but still compliant. I’m curious: how are you (or your teams) using AI or LLMs to improve data governance, cost efficiency, or platform observability? #DataGovernance #Snowflake #AI #CortexAI #GenAI #DataArchitecture #CostSavings

Ever ran search queries and got irrelevant hits because the system only matched keywords—not meaning? For technical pros, that’s a big blocker: you want fast, semantically rich retrieval across documents, images, or code, not just exact string matches. What is a Vector Database & Why It’s Critical A vector database is a specialised system that stores, indexes, and queries high-dimensional vector embeddings. It unlocks similarity search, handles unstructured data, and supports modern AI workflows. Key Concepts & Benefits 🔹 ID, Dimensions, Payload: Each vector entry has a unique identifier, a fixed number of dimensions (features), and payload/metadata for filtering or context. 🔹 Similarity Search & Indexing: Use algorithms like Approximate Nearest Neighbor (ANN), HNSW, PQ, LSH, etc., to quickly find nearby vectors. 🔹 Unstructured Data Handling: Text, images, audio—all converted into embeddings so you can store and search them semantically. Traditional databases struggle here. 🔹 Performance & Scalability: Horizontal scaling, metadata filters, real-time updates, and ability to support high query loads without huge latency. Start by embedding your data; then pick a vector DB that supports your scale, filters, and speed needs. Once in place, you’ll get more relevant results, less noise, and powerful AI-enabled use cases. 🎥 Watch Now on YouTube: https://lnkd.in/dpxmq7pZ #edquest #VectorDatabase #AIInfra #SimilaritySearch #Embeddings #MachineLearning #SemanticSearch #TechDeepDive

📝 Can AI transform Documentation and Metadata Management? Absolutely. Traditional documentation — static wikis, manual lineage diagrams, scattered notes — can’t keep pace with the speed of modern data ecosystems. They’re tedious, error-prone, and often outdated the moment they’re published. But AI is rewriting the playbook. From auto-generating lineage graphs to inferring entity relationships and writing human-readable column descriptions, AI is turning documentation from a chore into a living, intelligent asset. Imagine documentation that isn’t static — but evolves with your pipelines. ✅ Automated lineage extraction directly from SQL, Spark, and orchestration code ✅ Intelligent entity and relationship detection for better discoverability ✅ NLP-powered column descriptions that improve clarity and self-service analytics ✅ Continuous metadata updates in sync with evolving schemas and jobs This isn’t just efficiency. It’s the foundation for trust, governance, and collaboration in modern data teams. 🔍 Explore how AI is reshaping metadata management from a bottleneck into a strategic enabler in the latest blog (Part 6 of the AI-Augmented Data Engineering Series): 👉 https://lnkd.in/gPcp6euG #VoicesOfNeurealm #AIinData #DataEngineering #MetadataManagement #Governance #Innovation #ThoughtLeadership #Documentation

𝗣𝗮𝗿𝘁#𝟳: 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗹𝗶𝗻𝗴 --> 𝗢𝗯𝘀𝗼𝗹𝗲𝘁𝗲 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗹𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 🚨 𝗡𝗼𝘁 𝗮𝗹𝗹 𝗱𝗮𝘁𝗮 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 𝗮𝗴𝗲 𝗴𝗿𝗮𝗰𝗲𝗳𝘂𝗹𝗹𝘆. Some were powerful in their time, but in today’s Data & AI landscape… they’ve become obsolete. 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 𝘁𝗵𝗮𝘁 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝘀𝘁𝗮𝗻𝗱 the test of scale, flexibility, or modern architectures include:  • NIAM  • ORM  • Hierarchical Data Modelling  • Network Data Modelling  • Object-Oriented Data Modelling 𝗪𝗵𝘆? Because today’s demands — streaming, real-time analytics, federated architectures, lakehouse, and AI-driven use cases — require models that can adapt, scale, and integrate seamlessly. The world has moved to 𝗳𝗮𝗰𝘁-𝗼𝗿𝗶𝗲𝗻𝘁𝗲𝗱, 𝗲𝗻𝘀𝗲𝗺𝗯𝗹𝗲, 𝗮𝗻𝗱 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵𝗲𝘀. Legacy methods can’t keep up. How are you handling outdated techniques in your stack? Would you trust an agentic AI to refactor or re-model legacy designs? Agree or challenge — I’d love your lens on this. Seen this in your org? How did you approach it? #DataModeling #DataArchitecture #AI #Lakehouse #DataEngineering

Knowledge Graphs are a powerful alternative to the traditional vector DB based RAG . However , from our experience there are particular use cases that shine with Knowledge Graphs and some for which KGs are not always the right tool. These are few cases where Knowledge Graphs are unsuitable ❌ Data is flat and transactional (e.g., simple rows in a database). A relational DB is faster and simpler. ❌ Relationships don’t matter. If you only need metrics, aggregations, or Insert/Select/Update/Delete operations, a graph adds overhead. ❌ Low data complexity. If your dataset is small and doesn’t evolve much, the cost of building/maintaining a graph outweighs benefits. ❌ Lack of governance. Poorly managed vocabularies, ontologies, or metadata will make the graph confusing rather than insightful. ❌ Performance is critical for heavy analytics. Graph traversal can be slower than optimized columnar or in-memory DBs for certain workloads. #DataEngineering #AI #DataArchitecture #KnowledgeManagement #GraphDatabases #DataScience #EnterpriseAI

⚙️ What is SQLv2? The Open Standard for AI-Native Databases Traditional SQL stops at data. SQLv2 brings intelligence into the database. 🧠 What It Does SQLv2 extends ANSI SQL with: ✅ In-Engine ML Inference – run models directly in SQL ✅ First-Class Vector Search – similarity queries built-in ✅ Generative Functions – GENERATE_TEXT, SUMMARIZE, CLASSIFY ✅ Multimodal Data Support – images, audio, docs, and rows together ✅ Zero-Copy Execution – no data movement, minimal latency ✅ GPU/CPU Acceleration – native tensor and vector math ⚡ Why Now AI workloads break when data moves between systems. SQLv2 keeps inference where the data lives inside the database. That means faster, safer, and more predictable AI at scale. 💡 Example Query SELECT    customer_id,   PREDICT('churn_model', customer_features) AS churn_risk,   GENERATE_TEXT('Offer for', segment) AS personalized_offer FROM customers WHERE embedding <=> EMBED('high-value behavior') > 0.85   AND last_purchase < CURRENT_DATE - INTERVAL '30 days'; Prediction. Generation. Vector search. All in one query. All in-engine. 🔗 Learn More 🌐 Overview → synapcores.com/sqlv2 🚀 Beta Access → https://lnkd.in/dwQuy6ez SQLv2 isn’t another AI add-on. It’s the next chapter in SQL. #SQLv2 #AI #Database #MachineLearning #VectorSearch #SynapCores #OpenStandard

AI Automation Post: "Discover how AI is revolutionizing SQL automation, making data querying smarter and more intuitive. Embrace the future of data with AI-powered SQL tools! #AI #Automation #SQL"

⚡ Building even a basic 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) system is 𝗺𝘂𝗰𝗵 𝗵𝗮𝗿𝗱𝗲𝗿 than it looks. If you’re serious about deploying one, here’s why, and what are the critical moving parts you’ll need to get right 👇 📝 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 A) 𝗟𝗟𝗠 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 B) 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 – Just because you have context doesn’t mean prompts become trivial. You still need to: - Align outputs with business needs - Prevent jailbreaks & misuse - Design structured, repeatable outputs 🔍 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 C) 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 – Choosing the right model to represent your data in latent space. Contextual embeddings can make or break retrieval quality. D) 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 – It’s not just “pick Pinecone or Chroma.” You need to think about: - Where to host it - What metadata to store alongside vectors - Indexing strategies for speed vs. recall E) 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝗲𝗮𝗿𝗰𝗵 – Key search decisions include: - Similarity metric (cosine, dot, L2) - Query path (metadata-first vs ANN-first) - Hybrid search combinations F) 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 – Deciding how to break data into chunks for retrieval. - Small vs. large chunks - Sliding vs. tumbling windows - Retrieve only direct chunks, or pull parent/linked ones too G) 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗛𝗲𝘂𝗿𝗶𝘀𝘁𝗶𝗰𝘀 – Business logic matters as much as algorithms. Examples: - Time decay (freshness of data) - Re-ranking retrieved results - Removing duplicates, ensuring diversity - Conditional preprocessing before queries 🔒 𝗧𝗵𝗲 𝗙𝗼𝗿𝗴𝗼𝘁𝘁𝗲𝗻 𝗣𝗮𝗿𝘁 H) 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻, 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 - Track how retrieval + generation actually behave in production - Measure drift, errors, failures, and latency - Apply guardrails to prevent unsafe, biased, or incorrect outputs ⚡ 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: RAG is powerful, but it’s not plug-and-play. It’s an engineering discipline that blends retrieval, LLMOps, and product-specific heuristics. #AI #RAG #AIagents #LLMOps #MachineLearning #EnterpriseAI

Can a chatbot truly understand your data? I decided to find out using Snowflake Cortex Search. The results were eye-opening 👇 1. Data Preparation – Collect, clean, and store your data in Snowflake for processing. 2. Cortex Search Setup – Enable and configure Snowflake Cortex Search services. 3. Indexing – Generate embeddings and create searchable indexes for your data. 4. Query Setup – Define chatbot query flow and connect it to Cortex Search APIs. 5. LLM Integration – Combine Cortex Search with an LLM to generate contextual answers. 6. Chatbot Development – Build and integrate the chatbot interface with backend logic. 7. Testing & Validation – Verify chatbot accuracy and refine prompts or data. 8. Deployment – Launch the chatbot and monitor real-time performance for improvements. #keeplearning #ai