"SQL Server 2025: Developer-first features and AI capabilities"

Databricks AI/BI: 3 takeaways you can use now: • Embed governed dashboards in customer/partner apps. No per-viewer licenses. • Auto-post dashboard snapshots to Slack for faster decisions. • Conversational analytics with explainability and governance, not guesswork. Which one would help your team this quarter? https://lnkd.in/gW7ByFbZ #Databricks #AIBI #Analytics #DataGovernance #BusinessOutcomes

A new Power BI blog has been published: In today’s data-driven world, semantic models have become the backbone of trustworthy analytics. They define business logic, metrics, and relationships that turn raw data into meaningful, trusted and curated insights. As organizations embrace generative AI, semantic models provide the structure and context that AI needs to deliver accurate, reliable answers.  At Microsoft, we have spent nearly two decades refining the semantic layer that connects data … p class="link-more"a href="https://lnkd.in/gNUBuqrH" class="more-link"Continue readingspan class="screen-reader-text" “Microsoft named Leader and Outperformer in the 2025 GigaOm Radar for Semantic Layers & Metric Stores “/span/a/p

In today’s data-driven world, semantic models have become the backbone of trustworthy analytics. They define business logic, metrics, and relationships that turn raw data into meaningful, trusted and curated insights. As organizations embrace generative AI, semantic models provide the structure and context that AI needs to deliver accurate, reliable answers.  At Microsoft, we have spent nearly two decades refining the semantic layer that connects data … Continue reading “Microsoft named Leader and Outperformer in the 2025 GigaOm Radar for Semantic Layers & Metric Stores “ [https://lnkd.in/gMmsFPfj] #MicrosoftFabric #PowerBI #MSFTAdvocate

The modern data stack is evolving quickly, and one of the most exciting shifts is how AI and data engineering are coming together to enable natural-language access to data. Using tools like dbt MCP, dbt’s semantic layer, and Snowflake’s AI capabilities, it’s now possible to build a RAG style pipeline that allows business teams to get insights simply by asking questions without writing SQL or navigating dashboards. This approach combines: > dbt for trusted transformations and governed metrics > Snowflake for secure data storage and AI functions > MCP as the bridge between your models and the AI layer > RAG to retrieve the right data and generate meaningful answers The result is a powerful, governed, and user-friendly way to interact with enterprise data in natural language. It’s exciting to see how these technologies are shaping the future of analytics. #DataEngineering #dbt #Snowflake #AI #RAG #SemanticLayer #ModernDataStack #Analytics

𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐀𝐈/𝐁𝐈 𝐎𝐜𝐭𝐨𝐛𝐞𝐫 2025 𝐑𝐞𝐥𝐞𝐚𝐬𝐞: 𝐒𝐦𝐚𝐫𝐭𝐞𝐫 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬, 𝐒𝐞𝐚𝐦𝐥𝐞𝐬𝐬 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧, 𝐚𝐧𝐝 𝐆𝐞𝐧𝐢𝐞 𝐔𝐩𝐠𝐫𝐚𝐝𝐞𝐬! Databricks continues to push boundaries with its latest AI/BI release. October's update brings powerful enhancements that make data driven decision making faster, smarter, and more collaborative:  • New dashboard visualizations like funnel and waterfall charts  • Slack channel subscriptions for real-time insights  • External embedding of dashboards into apps and websites  • Genie upgrades with expanded APIs, better benchmarking, and a smarter knowledge store  • Early look at Research Agent Mode and natural language dashboard authoring Explore the full release here: https://lnkd.in/dDMZfEku #Databricks #AI #BI #Genie #DataAnalytics #ProductUpdate #Lakehouse #BusinessIntelligence

Having worked for many years in financial reporting datamarts, IT Engineers know that bringing advanced machine learning used to be a monumental effort. Gemini BigQuery changes that entirely! 📢 This should be a game-changer for Data Analysts! The days of complex model training, validation, and deployment holding back data insights are over. Gemini BigQuery streamlines the BigQuery ML workflow, putting powerful AI capabilities directly into your hands. Now, easily leverage popular models like Linear Regression, XGBoost, and K-Means clustering with unprecedented ease! The process is straightforward: 1) Establish your resource connection & grant permissions. 2) Create your dataset. 3) Build your model. 4) Make powerful predictions with your data. This empowers data professionals to rapidly generate deeper insights and drive smarter decisions without the traditional ML overhead. #BigQuery #Gemini #MachineLearning #DataAnalytics #GoogleCloud #AI #MLOps #FinancialReporting #BigQueryML

It can be hard to keep up with Databricks' speed of innovation and the impact it has on the tools you use, so here's a great blog summarizing "What's New" in October with AI/BI. Some great updates on external embedding, conversation API Updates (this is great for custom apps), and built-in knowledge extraction. https://lnkd.in/ei_4CGhf

🚀 Exploring the Future of Data: From Star Schemas to Vector Databases! 🚀 Just finished an insightful lecture on data warehousing and vector databases, and I’m excited to share some highlights: 🔹 Data Warehousing & Star Schema: Central fact tables connected to denormalized dimension tables enable faster and more flexible analytical queries. Surrogate keys improve query performance by replacing complex business keys with system-generated numeric keys. Finding the right granularity balances detail with performance for better decision-making. 🔹 Vector Databases & Embeddings: Move beyond traditional SQL limitations to semantic search powered by high-dimensional embeddings. Enable fuzzy, multilingual, and context-aware search—perfect for e-commerce, AI chatbots, and more. Techniques like cosine similarity and Euclidean distance help find meaning, not just exact matches. This blend of structured data design and cutting-edge semantic search is shaping the future of AI and business intelligence. Excited to learn more about how Retrieval-Augmented Generation (RAG) builds on this foundation! #DataWarehousing #VectorDatabases #AI #MachineLearning #SemanticSearch #DataScience #BusinessIntelligence #BigData #RAG

Has anyone had a chance to play with the new AI Functions in Microsoft Fabric? I’ve been exploring how they fit into ETL workflows and it’s been interesting. You can now call AI directly from your data pipelines. No need to spin up models or wire up external services. Just write a line of code and Fabric takes care of the rest. Some use-cases I’ve seen out there include: • Summarizing messy customer feedback before pushing it into Power BI • Auto-tagging product descriptions to improve search • Sentiment analysis on support tickets to help triage faster It’s early days, but this feels like it might spark a few interesting ideas. Instead of exporting data to AI, we’re bringing AI to the data. And it’s all happening inside the same workspace. Read more here: https://lnkd.in/gKEa5Tc8 Curious to hear from others. What have you built with it? Any surprises or lessons? #MicrosoftFabric #DataEngineering #ETL #AI

Business Intelligence is no longer about static dashboards and parameterized widgets. Earlier, BI meant data pipelines, aggregations, and visualizations. Then came Machine Learning, with tools like AWS SageMaker integrated into QuickSight. Now, Generative AI has redefined BI entirely, no fixed widgets, no predefined dashboards. Charts and insights can be created on the fly through natural language. Modern BI goes beyond internal data pipelines, integrating external APIs, real-time sources, and even GenAI-generated data from systems like MCP servers. We’re proud to build in this next-gen BI space like our CCE Chatbot, which answers complex business queries via LangGraph + OpenAI GPT-4o + FastAPI, combining analytics and AI reasoning in real time. #AI #GenerativeAI #BusinessIntelligence #LangGraph #OpenAI #GPT4o #Analytics #DataEngineering #MachineLearning #AWS #SageMaker #Quicksight #FastAPI #vfirstt