✨ FabCon + SQLCon 2026: Partner Know Before You Go Now Live!
Heading to FabCon + SQLCon 2026 in Atlanta? We’ve put together your full Partner Know Before You Go Guide — everything you need to navigate the week, maximize value, and make the most of all partner‑exclusive opportunities. 🚀 Here’s what you’ll find inside: 📅 Partner Day (Mar 17) — A full day of partner‑only learning, strategy, and networking. - SOLD OUT! 🍻 Partner Happy Hour — Connect with other partners and the Microsoft team. 🎤 AMA with the Fabric Partner Success Team — Bring your biggest questions around Data & AI priorities, partner opportunities, funding, enablement, and certifications. 🤝 1:1 Meetings — Book time with Fabric LT or the Partner Success Team to accelerate your strategy and unlock customer wins. 🎤 Partner Elevator Pitch Search — Submit your 2‑min pitch to be featured live on stage! 🔥 Partner Social Media Sprint — Daily LinkedIn challenge + chances to win Fabric SWAG. 🎓 Certification Spotlight — Get recognized during Arun’s keynote for hitting 100+ Fabric certs. 🎥 Testimonial Videos — Share your Fabric customer stories with global visibility. 📱 Whova Event App — Build your schedule, access slides, network, and join the partner community inside the app. 📘The full KBYG guide is attached below.Join the Fabric Partner Community for this Week's Fabric Engineering Connection Calls
🚀 Excited to share this week’s Fabric Engineering Connection lineup for our Microsoft Fabric partner community! We’ll be joined by Johannes Kebeck, Principal PM Manager, to dive into Fabric Maps — and how partners can bring location intelligence and rich geospatial experiences into their Fabric solutions. 📍 Fabric Engineering Connection – This Week 🌎 Americas & EMEA 📅 Wednesday, February 11 ⏰ 8:00–9:00 am PT 🌏 APAC 📅 Thursday, February 12 ⏰ 1:00–2:00 am UTC (Wednesday, February 11, 5:00–6:00 pm PT) These weekly calls are a chance for partners to: ✅ Hear directly from Fabric engineering ✅ See what’s new and what’s coming ✅ Ask questions and share feedback To participate in these calls, you must be a member of the Fabric Partner Community Teams channel. 👉 Join here: https://aka.ms/JoinFabricPartnerCommunity🎤 Save the Date: AMA with the Microsoft Fabric Leadership Team!
We’re excited to announce an upcoming Ask Me Anything (AMA) session with the Fabric Leadership Team—a unique opportunity for partners to engage directly with the leaders shaping the future of Microsoft Fabric! 🔹 Featured Leader Bogdan Crivat, Corporate Vice President, Azure Data Analytics 🗓️ Date & Time Tuesday, February 17 8:00–9:00 AM PT 💬 Ask Your Questions Have topics you want the leadership team to address? Submit or upvote your data analytics–related questions now: 👉 https://aka.ms/AMAwithFabricLT 👥 Who Can Join? This AMA is exclusive to members of the Fabric Partner Community. If you're not yet a member, join here to participate in future calls: 👉 https://aka.ms/JoinFabricPartnerCommunity🚀 FabCon + SQLCon Partner Social Sprint
A 4‑Day LinkedIn Challenge for Partners Attending FabCon + SQLCon in Atlanta If you’re joining us in Atlanta this March for FabCon + SQLCon 2026, we’ve got a new way for you to amplify your impact: the Partner Social Sprint — a daily LinkedIn challenge designed to spotlight partner voices, share real‑world insights, and have some fun along the way. Whether you’re presenting, staffing a booth, or attending sessions, this is your chance to tell your story and connect with the global Microsoft Fabric & SQL community. 🔥 What Is the Partner Social Sprint? A 4‑day LinkedIn posting challenge running during FabCon + SQLCon 2026 in Atlanta. Each day has a theme, and every post earns you entries to win exclusive Fabric SWAG, including the fan‑favorite Fabric Kicks. 🗓 Daily Themes (Conference Week) Tuesday, March 17 – Day 0 (Travel / Partner Day) Why I’m going / why I’m here Wednesday, March 18 – Day 1 One big learning Thursday, March 19 – Day 2 Build your practice Friday, March 20 – Day 3 Customer impact ✅ How It Works If you’re a partner attending FabCon + SQLCon in Atlanta: Post once per day on LinkedIn during the event, following the daily theme. Use the hashtags: #FabConSQLCon2026 #PartnerSocialSprintSweepstakes #MicrosoftPartner Tag the FabCon & SQLCon – The Microsoft Fabric & SQL Community Conferences LinkedIn page. Submit all your LinkedIn post URLs via the entry form. 👉 Each post = 1 entry, and if you post all 4 days, you’ll get 1 bonus entry. 🏆 What You Can Win Fabric SWAG Fabric Kicks Recognition in the Fabric Partner Community and across our channels 🌐 Who Should Join? Microsoft partners attending FabCon + SQLCon in Atlanta Partner sellers, architects, engineers, and community champions who want to: Share key learnings in real time Highlight their Fabric & SQL practices Showcase customer success and impact 👉 Ready to Sprint With Us? Get all the details and submit your posts here: https://aka.ms/PartnerSocialSprint Let’s light up LinkedIn with the stories, insights, and innovation coming out of FabCon + SQLCon. We can’t wait to see your posts from Atlanta!Join the Fabric Partner Community for this Week's Fabric Engineering Connection calls!
The Fabric partner ecosystem is buzzing right now — and 2026 is already raising the bar. 🚀 On this week's Fabric Engineering Connection call, Tamer Farag will share what’s next for partners across skilling, demos, FabCon + SQLCon Atlanta, and more. Highlights include: 🎓 More skilling momentum (DP‑600/DP‑700 vouchers, new Partner Project Ready workshops, and a new “Chat with your Data in a Day” xIAD workshop). 🔦 Fabric Certification Spotlight: partners who reach 100+ Fabric certifications will be recognized live in Arun’s keynote at FabCon + SQLCon. 🤝 New ways to tell your story and win with customers through Fabric Demo eXperiences, Fabric Featured Partners + case studies, and FabCon experiences (Partner Elevator Pitch Search, 1:1 + executive meetings, testimonial videos, the Partner Social Sprint, and more). If you’re a Microsoft partner investing in Fabric, we’d love for you to join our next Fabric Engineering Connection call: 📅 Americas/EMEA – Wednesday, Feb 4, 8–9 AM PT 📅 APAC – Thursday, Feb 5, 1–2 AM UTC (Wednesday, Feb 4, 5–6 PM PT) To join, you must be a member of the Fabric Partner Community in Teams: https://aka.ms/JoinFabricPartnerCommunityAzure AI Foundry vs. Azure Databricks – A Unified Approach to Enterprise Intelligence
Key Insights into Azure AI Foundry and Azure Databricks Complementary Powerhouses: Azure AI Foundry is purpose-built for generative AI application and agent development, focusing on model orchestration and rapid prototyping, while Azure Databricks excels in large-scale data engineering, analytics, and traditional machine learning, forming the data intelligence backbone. Seamless Integration for End-to-End AI: A critical native connector allows AI agents developed in Foundry to access real-time, governed data from Databricks, enabling contextual and data-grounded AI solutions. This integration facilitates a comprehensive AI lifecycle from data preparation to intelligent application deployment. Specialized Roles for Optimal Performance: Enterprises leverage Databricks for its robust data processing, lakehouse architecture, and ML model training capabilities, and then utilize AI Foundry for deploying sophisticated generative AI applications, agents, and managing their lifecycle, ensuring responsible AI practices and scalability. In the rapidly evolving landscape of artificial intelligence, organizations seek robust platforms that can not only handle vast amounts of data but also enable the creation and deployment of intelligent applications. Microsoft Azure offers two powerful, yet distinct, services in this domain: Azure AI Foundry and Azure Databricks. While both contribute to an organization's AI capabilities, they serve different primary functions and are designed to complement each other in building comprehensive, enterprise-grade AI solutions. Decoding the Core Purpose: Foundry for Generative AI, Databricks for Data Intelligence At its heart, the distinction between Azure AI Foundry and Azure Databricks lies in their core objectives and the types of workloads they are optimized for. Understanding these fundamental differences is crucial for strategic deployment and maximizing their combined potential. Azure AI Foundry: The Epicenter for Generative AI and Agents Azure AI Foundry emerges as Microsoft's unified platform specifically engineered for the development, deployment, and management of generative AI applications and AI agents. It represents a consolidation of capabilities from what were formerly Azure AI Studio and Azure OpenAI Studio. Its primary focus is on accelerating the entire lifecycle of generative AI, from initial prototyping to large-scale production deployments. Key Characteristics of Azure AI Foundry: Generative AI Focus: Foundry streamlines the development of large language models (LLMs) and customized generative AI applications, including chatbots and conversational AI. It emphasizes prompt engineering, Retrieval-Augmented Generation (RAG), and agent orchestration. Extensive Model Catalog: It provides access to a vast catalog of over 11,000 foundation models from various publishers, including OpenAI, Meta (Llama 4), Mistral, and others. These models can be deployed via managed compute or serverless API deployments, offering flexibility and choice. Agentic Development: A significant strength of Foundry is its support for building sophisticated AI agents. This includes tools for grounding agents with knowledge, tool calling, comprehensive evaluations, tracing, monitoring, and guardrails to ensure responsible AI practices. Foundry Local further extends this by allowing offline and on-device development. Unified Development Environment: It offers a single management grouping for agents, models, and tools, promoting efficient development and consistent governance across AI projects. Enterprise Readiness: Built-in capabilities such as Role-Based Access Control (RBAC), observability, content safety, and project isolation ensure that AI applications are secure, compliant, and scalable for enterprise use. Figure 1: Conceptual Architecture of Azure AI Foundry illustrating its various components for AI development and deployment. Azure Databricks: The Powerhouse for Data Engineering, Analytics, and Machine Learning Azure Databricks, on the other hand, is an Apache Spark-based data intelligence platform optimized for large-scale data engineering, analytics, and traditional machine learning workloads. It acts as a collaborative workspace for data scientists, data engineers, and ML engineers to process, analyze, and transform massive datasets, and to build and deploy diverse ML models. Key Characteristics of Azure Databricks: Unified Data Analytics Platform: Central to Databricks is its lakehouse architecture, built on Delta Lake, which unifies data warehousing and data lakes. This provides a single platform for data engineering, SQL analytics, and machine learning. Big Data Processing: Excelling in distributed computing, Databricks is ideal for processing large datasets, performing ETL (Extract, Transform, Load) operations, and real-time analytics at scale. Comprehensive ML and AI Workflows: It offers a specialized environment for the full ML lifecycle, including data preparation, feature engineering, model training (both classic and deep learning), and model serving. Tools like MLflow are integrated for tracking, evaluating, and monitoring ML models. Data Intelligence Features: Databricks includes AI-assistive features such as Databricks Assistant and Databricks AI/BI Genie, which enable users to interact with their data using natural language queries to derive insights. Unified Governance with Unity Catalog: Unity Catalog provides a centralized governance solution for all data and AI assets within the lakehouse, ensuring data security, lineage tracking, and access control. Figure 2: The Databricks Data Intelligence Platform with its unified approach to data, analytics, and AI. The Symbiotic Relationship: Integration and Complementary Use Cases While distinct in their primary functions, Azure AI Foundry and Azure Databricks are explicitly designed to work together, forming a powerful, integrated ecosystem for end-to-end AI development and deployment. This synergy is key to building advanced, data-driven AI solutions in the enterprise. Seamless Integration for Enhanced AI Capabilities The integration between the two platforms is a cornerstone of Microsoft's AI strategy, enabling AI agents and generative applications to be grounded in high-quality, governed enterprise data. Key Integration Points: Native Databricks Connector in AI Foundry: A significant development in 2025 is the public preview of a native connector that allows AI agents built in Azure AI Foundry to directly query real-time, governed data from Azure Databricks. This means Foundry agents can leverage Databricks AI/BI Genie to surface data insights and even trigger Databricks Jobs, providing highly contextual and domain-aware responses. Data Grounding for AI Agents: This integration enables AI agents to access structured and unstructured data processed and stored in Databricks, providing the necessary context and knowledge base for more accurate and relevant generative AI outputs. All interactions are auditable within Databricks, maintaining governance and security. Model Crossover and Availability: Foundation models, such as the Llama 4 family, are made available across both platforms. Databricks DBRX models can also appear in the Foundry model catalog, allowing flexibility in where models are trained, deployed, and consumed. Unified Identity and Governance: Both platforms leverage Azure Entra ID for authentication and access control, and Unity Catalog provides unified governance for data and AI assets managed by Databricks, which can then be respected by Foundry agents. Here's a breakdown of how a typical flow might look: Mindmap 1: Illustrates the complementary roles and integration points between Azure Databricks and Azure AI Foundry within an end-to-end AI solution. When to Use Which (and When to Use Both) Choosing between Azure AI Foundry and Azure Databricks, or deciding when to combine them, depends on the specific requirements of your AI project: Choose Azure AI Foundry When You Need To: Build and deploy production-grade generative AI applications and multi-agent systems. Access, evaluate, and benchmark a wide array of foundation models from various providers. Develop AI agents with sophisticated capabilities like tool calling, RAG, and contextual understanding. Implement enterprise-grade guardrails, tracing, monitoring, and content safety for AI applications. Rapidly prototype and iterate on generative AI solutions, including chatbots and copilots. Integrate AI agents deeply with Microsoft 365 and Copilot Studio. Choose Azure Databricks When You Need To: Perform large-scale data engineering, ETL, and data warehousing on a unified lakehouse. Build and train traditional machine learning models (supervised, unsupervised learning, deep learning) at scale. Manage and govern all data and AI assets centrally with Unity Catalog, ensuring data quality and lineage. Conduct complex data analytics, business intelligence (BI), and real-time data processing. Leverage AI-assistive tools like Databricks AI/BI Genie for natural language interaction with data. Require high-performance compute and auto-scaling for data-intensive workloads. Use Both for Comprehensive AI Solutions: The most powerful approach for many enterprises is to leverage both platforms. Azure Databricks can serve as the robust data backbone, handling data ingestion, processing, governance, and traditional ML model training. Azure AI Foundry then sits atop this foundation, consuming the prepared and governed data to build, deploy, and manage intelligent generative AI agents and applications. This allows for: Domain-Aware AI: Foundry agents are grounded in enterprise-specific data from Databricks, leading to more accurate, relevant, and trustworthy AI responses. End-to-End AI Lifecycle: Databricks manages the "data intelligence" part, and Foundry handles the "generative AI application" part, covering the entire spectrum from raw data to intelligent user experience. Optimized Resource Utilization: Each platform focuses on what it does best, leading to more efficient resource allocation and specialized toolsets for different stages of the AI journey. Comparative Analysis: Features and Capabilities To further illustrate their distinct yet complementary nature, let's examine a detailed comparison of their features, capabilities, and typical user bases. Radar Chart 1: This chart visually compares Azure AI Foundry and Azure Databricks across several key dimensions, illustrating their specialized strengths. Azure AI Foundry excels in generative AI and agent orchestration, while Azure Databricks dominates in data engineering, unified data governance, and traditional ML workflows. A Detailed Feature Comparison Feature Category Azure AI Foundry Azure Databricks Primary Focus Generative AI application & agent development, model orchestration Large-scale data engineering, analytics, traditional ML, and AI workflows Data Handling Connects to diverse data sources (e.g., Databricks, Azure AI Search) for grounding AI agents. Not a primary data storage/processing platform. Native data lakehouse architecture (Delta Lake), optimized for big data processing, storage, and real-time analytics. AI/ML Capabilities Foundation models (LLMs), prompt engineering, RAG, agent orchestration, model evaluation, content safety, responsible AI tooling. Traditional ML (supervised/unsupervised), deep learning, feature engineering, MLflow for lifecycle management, Databricks AI/BI Genie. Development Style Low-code agent building, prompt flows, unified SDK/API, templates. Code-first (Python, SQL, Scala, R), notebooks, IDE integrations. Model Access & Deployment Extensive model catalog (11,000+ models), serverless API, managed compute deployments, model benchmarking. Training and serving custom ML models, including deep learning. Models available for deployment through MLflow. Governance & Security Azure-based security & compliance, RBAC, project isolation, content safety guardrails, tracing, evaluations. Unity Catalog for unified data & AI governance, lineage tracking, access control, Entra ID integration. Key Users AI developers, business analysts, citizen developers, AI app builders. Data scientists, data engineers, ML engineers, data analysts. Integration Points Native connector to Databricks AI/BI Genie, Azure AI Search, Microsoft 365, Copilot Studio, Power Platform. Microsoft Fabric, Power BI, Azure AI Foundry, Azure Purview, Azure Monitor, Azure Key Vault. Table 1: A comparative overview of the distinct features and functionalities of Azure AI Foundry and Azure Databricks Concluding Thoughts In essence, Azure AI Foundry and Azure Databricks are not competing platforms but rather essential components of a unified, comprehensive AI strategy within the Azure ecosystem. Azure Databricks provides the robust, scalable foundation for all data engineering, analytics, and traditional machine learning workloads, acting as the "data intelligence platform." Azure AI Foundry then leverages this foundation to specialize in the rapid development, deployment, and operationalization of generative AI applications and intelligent agents. Together, they enable enterprises to unlock the full potential of AI, transforming raw data into powerful, domain-aware, and governed intelligent solutions. Frequently Asked Questions (FAQ) What is the main difference between Azure AI Foundry and Azure Databricks? Azure AI Foundry is specialized for building, deploying, and managing generative AI applications and AI agents, focusing on model orchestration and prompt engineering. Azure Databricks is a data intelligence platform for large-scale data engineering, analytics, and traditional machine learning, built on a Lakehouse architecture. Can Azure AI Foundry and Azure Databricks be used together? Yes, they are designed to work synergistically. Azure AI Foundry can leverage a native connector to access real-time, governed data from Azure Databricks, allowing AI agents to be grounded in enterprise data for more accurate and contextual responses. Which platform should I choose for training large machine learning models? For training large-scale, traditional machine learning, and deep learning models, Azure Databricks is generally the preferred choice due to its robust capabilities for data processing, feature engineering, and ML lifecycle management (MLflow). Azure AI Foundry focuses more on the deployment and orchestration of pre-trained foundation models and generative AI applications. Does Azure AI Foundry replace Azure Machine Learning or Databricks? No, Azure AI Foundry complements these services. It provides a specialized environment for generative AI and agent development, often integrating with data and models managed by Azure Databricks or Azure Machine Learning for comprehensive AI solutions. How do these platforms handle data governance? Azure Databricks utilizes Unity Catalog for unified data and AI governance, providing centralized control over data access and lineage. Azure AI Foundry integrates with Azure-based security and compliance features, ensuring responsible AI practices and data privacy within its generative AI applications.Quarterly AMA with Amir Netz – Microsoft Fabric Partners, Don’t Miss Out!
🌟 Microsoft Partners—don’t miss your chance to connect directly with Amir Netz, CTO & Technical Fellow, at our exclusive Quarterly AMA (Ask Me Anything) session! The Fabric Engineering Connection is your gateway to insider insights, direct access to product leaders, and the latest updates on Microsoft Fabric. As a valued partner, you’ll have the opportunity to ask questions, share feedback, and learn about new features and strategies that can help you drive success for your clients and your business. This session is designed to empower partners with actionable knowledge and networking opportunities. 📅 Save the date: Americas & EMEA: Wednesday, January 21, 8–9 am PT APAC: Thursday, January 22, 1–2 am UTC / Americas & EMEA: Wednesday, January 21, 5–6 pm PT 🔗 Not a member yet? Join the Fabric Partner Community to participate: https://aka.ms/JoinFabricPartnerCommunity. Let’s build the future of data together!
Comparing Open-Source vs Closed LLMs for Enterprise Apps
LLMs are advanced AI models trained in vast data. They enable tasks such as summarization, translation, content creation, and data analysis. When companies build applications that use AI, one of the most important decisions they face is choosing the right type of Large Language Model (LLM). There are two main choices: open‑source LLMs and closed or proprietary LLMs. Understanding the differences between them helps businesses decide which option fits their needs, goals, and security requirements.Join the Fabric Partner Community for this Week's Fabric Engineering Connection calls!
🚀 Upcoming Fabric Engineering Connection Call – Americas & EMEA & APAC! Join us on Wednesday, January 14, 8–9 am PT (Americas & EMEA) and Thursday, January 15, 1–2 am UTC (APAC) for a special session featuring the latest Power BI Updates & Announcements from Ignite with Sujata Narayana, Rui Romano, and other members of the Power BI Product Team. Plus, hear from Tom Peplow on Developing Apps on OneLake APIs. 🔗 To participate, make sure you’re a member of the Fabric Partner Community Teams Channel. If you haven’t joined yet, sign up here: https://lnkd.in/g_PRdfjt Don’t miss this opportunity to learn, connect, and stay up to date with the latest in Microsoft Fabric and Power BI!Join the Fabric Partner Community for this Week's Fabric Engineering Connection calls!
🚀 Happy New Year to all the amazing Microsoft partners I've had the privilege to work with during 2025. I'm excited to announce the first presenters of 2026 for this week's Fabric Engineering Connection calls! Join us Wednesday, January 7, from 8–9 am PT (Americas & EMEA) and Thursday, January 8, from 1–2 am UTC (APAC) for insightful sessions from Yaron Canari and Benny Austin This week’s focuses: 🎯 Americas & EMEA: Discover, Manage and Govern Fabric Data with OneLake Catalog 🎯 APAC: Updates and Enhancements made to the Fabric Accelerator This is your opportunity to learn more, ask questions, and provide feedback. To participate in the call, you must be a member of the Fabric Partner Community Teams channel. To join, complete the participation form at https://aka.ms/JoinFabricPartnerCommunity. We look forward to seeing you at the calls!
