|
| 1 | +{ |
| 2 | + "live": [ |
| 3 | + { |
| 4 | + "time": "9:00 AM", |
| 5 | + "title": "Azure Cosmos DB Conf 2026 Keynote", |
| 6 | + "description": "Kirill hosts a keynote conversation with leaders from Vercel, OpenAI, Walmart, and AMD on building modern applications, AI, and data at planetary scale.", |
| 7 | + "speakers": [ |
| 8 | + { "name": "Kirill Gavrylyuk", "slug": "kirill-gavrylyuk" }, |
| 9 | + { "name": "Guillermo Rauch", "slug": "guillermo-rauch" }, |
| 10 | + { "name": "Jonathan Lee", "slug": "jonathan-lee" }, |
| 11 | + { "name": "Andrew Liu", "slug": "andrew-liu" }, |
| 12 | + { "name": "Steve Berg", "slug": "steve-berg" } |
| 13 | + ], |
| 14 | + "url": "" |
| 15 | + }, |
| 16 | + { |
| 17 | + "time": "9:50 AM", |
| 18 | + "title": "Featured Session", |
| 19 | + "description": "Sid Anand, Technical Fellow at Walmart, delivers a featured session — details coming soon.", |
| 20 | + "speakers": [{ "name": "Sid Anand", "slug": "sid-anand" }], |
| 21 | + "url": "" |
| 22 | + }, |
| 23 | + { |
| 24 | + "time": "10:15 AM", |
| 25 | + "title": "Performance-Boosting Memory Patterns: Save Tokens, Cut Costs, Increase Speed", |
| 26 | + "description": "Three production-ready memory patterns in Azure Cosmos DB that help AI systems retain context without blowing up token usage.", |
| 27 | + "speakers": [{ "name": "Chander Dhall", "slug": "chander-dhall" }], |
| 28 | + "url": "" |
| 29 | + }, |
| 30 | + { |
| 31 | + "time": "10:45 AM", |
| 32 | + "title": "Cutting AI Agent Costs with Azure Cosmos DB: The Agent Memory Fabric", |
| 33 | + "description": "How to unify semantic caching, Change Feed coordination, and optimistic concurrency into a single Agent Memory Fabric that slashes multi-agent costs.", |
| 34 | + "speakers": [{ "name": "Farah Abdou", "slug": "farah-abdou" }], |
| 35 | + "url": "" |
| 36 | + }, |
| 37 | + { |
| 38 | + "time": "11:10 AM", |
| 39 | + "title": "MultiCloudDB: Write Once. Run Anywhere.", |
| 40 | + "description": "A sneak preview of a portable Java SDK that runs unchanged across Azure Cosmos DB, Amazon DynamoDB, and Google Cloud Spanner.", |
| 41 | + "speakers": [{ "name": "Theo van Kraay", "slug": "theo-van-kraay" }], |
| 42 | + "url": "" |
| 43 | + }, |
| 44 | + { |
| 45 | + "time": "11:30 AM", |
| 46 | + "title": "Mastering the Azure Cosmos DB Change Feed: Patterns, Scaling, and Real-World Architectures", |
| 47 | + "description": "A deep dive into Change Feed internals, consumption models, lease management, and how to debug and scale it in production.", |
| 48 | + "speakers": [{ "name": "Justine Cocchi", "slug": "justine-cocchi" }], |
| 49 | + "url": "" |
| 50 | + }, |
| 51 | + { |
| 52 | + "time": "12:00 PM", |
| 53 | + "title": "One Codebase, Any Cloud: Building a Retail Database with OSS and Azure", |
| 54 | + "description": "Build a retail store database once with MongoDB-compatible APIs and deploy it unchanged on-prem via Kubernetes and fully managed on Azure.", |
| 55 | + "speakers": [{ "name": "Khelan Modi", "slug": "khelan-modi" }], |
| 56 | + "url": "" |
| 57 | + }, |
| 58 | + { |
| 59 | + "time": "12:25 PM", |
| 60 | + "title": "Querying and Indexing in Azure Cosmos DB: The Complete Guide", |
| 61 | + "description": "A complete tour of the Azure Cosmos DB query engine and indexing system — routing, index types, query metrics, and cost-saving optimizations.", |
| 62 | + "speakers": [{ "name": "James Codella", "slug": "james-codella" }], |
| 63 | + "url": "" |
| 64 | + }, |
| 65 | + { |
| 66 | + "time": "1:00 PM", |
| 67 | + "title": "Behind the Scenes: How Azure Cosmos DB Runs Under the Hood", |
| 68 | + "description": "A whiteboard walkthrough of Azure Cosmos DB's internals — replica sets, consistent hashing, Request Units, and RU pooling across a global fleet.", |
| 69 | + "speakers": [{ "name": "Andrew Liu", "slug": "andrew-liu" }], |
| 70 | + "url": "" |
| 71 | + }, |
| 72 | + { |
| 73 | + "time": "1:30 PM", |
| 74 | + "title": "Data Modeling Decisions That Make or Break Your Cosmos DB Application", |
| 75 | + "description": "Strategies for partition keys, schema design, storage estimation, and managing cross-partition queries before the choices become painful to fix.", |
| 76 | + "speakers": [{ "name": "Hasan Savran", "slug": "hasan-savran" }], |
| 77 | + "url": "" |
| 78 | + } |
| 79 | + ], |
| 80 | + "onDemand": [ |
| 81 | + { |
| 82 | + "title": "Distributed Locks, Sagas, and Coordination with Cosmos DB", |
| 83 | + "description": "Use Cosmos DB as a coordination layer for locks, leases, leader election, and saga orchestration — and understand the consistency tradeoffs at scale.", |
| 84 | + "speakers": [{ "name": "Eric Boyd", "slug": "eric-boyd" }], |
| 85 | + "url": "" |
| 86 | + }, |
| 87 | + { |
| 88 | + "title": "From Rising RU Costs to Stable Performance: A Practical Cosmos DB Case Study", |
| 89 | + "description": "A real-world walkthrough of diagnosing hot partitions, over-indexing, and throughput issues — and the optimizations that restored stable performance.", |
| 90 | + "speakers": [{ "name": "Anurag Dutt", "slug": "anurag-dutt" }], |
| 91 | + "url": "" |
| 92 | + }, |
| 93 | + { |
| 94 | + "title": "Know your user: Identity-aware MCP servers with Cosmos DB", |
| 95 | + "description": "Build a Python MCP server that authenticates users via Entra ID and stores per-user data securely in Azure Cosmos DB.", |
| 96 | + "speakers": [{ "name": "Pamela Fox", "slug": "pamela-fox" }], |
| 97 | + "url": "" |
| 98 | + }, |
| 99 | + { |
| 100 | + "title": "Securing Azure Cosmos DB: The Right Way", |
| 101 | + "description": "A cohesive framework for identity-first access, isolation, encryption, and data protection in cloud data workloads — built from real customer scenarios.", |
| 102 | + "speakers": [{ "name": "Sudhanshu Khera", "slug": "sudhanshu-khera" }], |
| 103 | + "url": "" |
| 104 | + }, |
| 105 | + { |
| 106 | + "title": "Real-Time Fraud Detection at Scale: Event Sourcing with Azure Cosmos DB", |
| 107 | + "description": "Combine event sourcing, Change Feed, and event-driven integration to build an auditable real-time fraud detection system across heterogeneous data sources.", |
| 108 | + "speakers": [{ "name": "Divakar Kumar", "slug": "divakar-kumar" }], |
| 109 | + "url": "" |
| 110 | + }, |
| 111 | + { |
| 112 | + "title": "Beyond Vector Search: What RAG Actually Needs", |
| 113 | + "description": "Extend a working AI agent with agentic RAG using Cosmos DB hybrid search — vector plus full-text — so it retrieves what actually matters.", |
| 114 | + "speakers": [{ "name": "Yohan Lasorsa", "slug": "yohan-lasorsa" }], |
| 115 | + "url": "" |
| 116 | + }, |
| 117 | + { |
| 118 | + "title": "From MongoDB to Azure DocumentDB: A Migration Playbook", |
| 119 | + "description": "A tour of migration options and tradeoffs for moving MongoDB workloads to Azure DocumentDB.", |
| 120 | + "speakers": [{ "name": "Sandeep Nair", "slug": "sandeep-nair" }], |
| 121 | + "url": "" |
| 122 | + }, |
| 123 | + { |
| 124 | + "title": "Designing Cost-Efficient, High-Scale Systems with Azure Cosmos DB: Lessons from Production", |
| 125 | + "description": "Repeatable data-modeling, TTL, Change Feed, and observability patterns that cut Cosmos DB costs without sacrificing performance or velocity.", |
| 126 | + "speakers": [{ "name": "Patrick Oguaju", "slug": "patrick-oguaju" }], |
| 127 | + "url": "" |
| 128 | + }, |
| 129 | + { |
| 130 | + "title": "Setting Up Your Azure Cosmos DB Development Environment (and Supercharging It with AI)", |
| 131 | + "description": "A complete Cosmos DB dev workflow — emulator, testing, CI/CD — supercharged with context files and prompting techniques for AI coding assistants.", |
| 132 | + "speakers": [{ "name": "Sajeetharan Sinnathurai", "slug": "sajeetharan-sinnathurai" }], |
| 133 | + "url": "" |
| 134 | + }, |
| 135 | + { |
| 136 | + "title": "From JOINs to JSON: Migrating a Real-World ASP.NET App to Cosmos DB with GitHub Copilot", |
| 137 | + "description": "A prompt-by-prompt playbook for migrating AdventureWorks and an ASP.NET EF Core app to .NET 9 on Cosmos DB using GitHub Copilot and Agent Kit.", |
| 138 | + "speakers": [{ "name": "Sergiy Smyrnov", "slug": "sergiy-smyrnov" }], |
| 139 | + "url": "" |
| 140 | + }, |
| 141 | + { |
| 142 | + "title": "Designing High-Scale Event-Driven Microservices with Azure Cosmos DB", |
| 143 | + "description": "Production patterns for building loosely coupled, event-driven microservices with Cosmos DB Change Feed as the domain event backbone.", |
| 144 | + "speakers": [{ "name": "Tural Suleymani", "slug": "tural-suleymani" }], |
| 145 | + "url": "" |
| 146 | + }, |
| 147 | + { |
| 148 | + "title": "Memory for Your Agents: Building Chat History and Semantic Caching with Azure Cosmos DB", |
| 149 | + "description": "Build chat history and semantic cache containers that give AI agents persistent, context-aware memory while cutting tokens and latency.", |
| 150 | + "speakers": [{ "name": "Lino Tadros", "slug": "lino-tadros" }], |
| 151 | + "url": "" |
| 152 | + }, |
| 153 | + { |
| 154 | + "title": "Importing Data into Azure Cosmos DB: Tools, Tradeoffs, and Capacity Planning", |
| 155 | + "description": "A decision framework for choosing between the Data Migration Tool, Azure Data Factory, and the Spark connector — plus capacity planning for bulk loads.", |
| 156 | + "speakers": [{ "name": "Rakhi Thejraj", "slug": "rakhi-thejraj" }], |
| 157 | + "url": "" |
| 158 | + } |
| 159 | + ] |
| 160 | +} |
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