|
25 | 25 | "title": "Performance-Boosting Memory Patterns: Save Tokens, Cut Costs, Increase Speed", |
26 | 26 | "description": "Three production-ready memory patterns in Azure Cosmos DB that help AI systems retain context without blowing up token usage.", |
27 | 27 | "speakers": [{ "name": "Chander Dhall", "slug": "chander-dhall" }], |
28 | | - "url": "" |
| 28 | + "url": "https://youtu.be/IdK3gm-dZ2k" |
29 | 29 | }, |
30 | 30 | { |
31 | 31 | "time": "10:45 AM", |
32 | 32 | "title": "Cutting AI Agent Costs with Azure Cosmos DB: The Agent Memory Fabric", |
33 | 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 | 34 | "speakers": [{ "name": "Farah Abdou", "slug": "farah-abdou" }], |
35 | | - "url": "" |
| 35 | + "url": "https://youtu.be/Wo34Trg0Wyg" |
36 | 36 | }, |
37 | 37 | { |
38 | 38 | "time": "11:10 AM", |
|
46 | 46 | "title": "Mastering the Azure Cosmos DB Change Feed: Patterns, Scaling, and Real-World Architectures", |
47 | 47 | "description": "A deep dive into Change Feed internals, consumption models, lease management, and how to debug and scale it in production.", |
48 | 48 | "speakers": [{ "name": "Justine Cocchi", "slug": "justine-cocchi" }], |
49 | | - "url": "" |
| 49 | + "url": "https://youtu.be/kFqcImlqYYs" |
50 | 50 | }, |
51 | 51 | { |
52 | 52 | "time": "12:00 PM", |
|
67 | 67 | "title": "Behind the Scenes: How Azure Cosmos DB Runs Under the Hood", |
68 | 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 | 69 | "speakers": [{ "name": "Andrew Liu", "slug": "andrew-liu" }], |
70 | | - "url": "" |
| 70 | + "url": "https://youtu.be/6POmAm--Kgk" |
71 | 71 | }, |
72 | 72 | { |
73 | 73 | "time": "1:30 PM", |
74 | 74 | "title": "Data Modeling Decisions That Make or Break Your Cosmos DB Application", |
75 | 75 | "description": "Strategies for partition keys, schema design, storage estimation, and managing cross-partition queries before the choices become painful to fix.", |
76 | 76 | "speakers": [{ "name": "Hasan Savran", "slug": "hasan-savran" }], |
77 | | - "url": "" |
| 77 | + "url": "https://youtu.be/FhgewWmQyBM" |
78 | 78 | } |
79 | 79 | ], |
80 | 80 | "onDemand": [ |
81 | 81 | { |
82 | 82 | "title": "Distributed Locks, Sagas, and Coordination with Cosmos DB", |
83 | 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 | 84 | "speakers": [{ "name": "Eric Boyd", "slug": "eric-boyd" }], |
85 | | - "url": "" |
| 85 | + "url": "https://youtu.be/wyBJmeXqbg8" |
86 | 86 | }, |
87 | 87 | { |
88 | 88 | "title": "From Rising RU Costs to Stable Performance: A Practical Cosmos DB Case Study", |
89 | 89 | "description": "A real-world walkthrough of diagnosing hot partitions, over-indexing, and throughput issues — and the optimizations that restored stable performance.", |
90 | 90 | "speakers": [{ "name": "Anurag Dutt", "slug": "anurag-dutt" }], |
91 | | - "url": "" |
| 91 | + "url": "https://youtu.be/YyV2gX9nNN4" |
92 | 92 | }, |
93 | 93 | { |
94 | 94 | "title": "Know your user: Identity-aware MCP servers with Cosmos DB", |
95 | 95 | "description": "Build a Python MCP server that authenticates users via Entra ID and stores per-user data securely in Azure Cosmos DB.", |
96 | 96 | "speakers": [{ "name": "Pamela Fox", "slug": "pamela-fox" }], |
97 | | - "url": "" |
| 97 | + "url": "https://youtu.be/YsYzykMRvgA" |
98 | 98 | }, |
99 | 99 | { |
100 | 100 | "title": "Securing Azure Cosmos DB: The Right Way", |
101 | 101 | "description": "A cohesive framework for identity-first access, isolation, encryption, and data protection in cloud data workloads — built from real customer scenarios.", |
102 | 102 | "speakers": [{ "name": "Sudhanshu Khera", "slug": "sudhanshu-khera" }], |
103 | | - "url": "" |
| 103 | + "url": "https://youtu.be/F4EqbcR80TA" |
104 | 104 | }, |
105 | 105 | { |
106 | 106 | "title": "Real-Time Fraud Detection at Scale: Event Sourcing with Azure Cosmos DB", |
107 | 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 | 108 | "speakers": [{ "name": "Divakar Kumar", "slug": "divakar-kumar" }], |
109 | | - "url": "" |
| 109 | + "url": "https://youtu.be/2x4Gv4GX3Ao" |
110 | 110 | }, |
111 | 111 | { |
112 | 112 | "title": "Beyond Vector Search: What RAG Actually Needs", |
113 | 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 | 114 | "speakers": [{ "name": "Yohan Lasorsa", "slug": "yohan-lasorsa" }], |
115 | | - "url": "" |
| 115 | + "url": "https://youtu.be/nqaUuSoB6I0" |
116 | 116 | }, |
117 | 117 | { |
118 | 118 | "title": "From MongoDB to Azure DocumentDB: A Migration Playbook", |
119 | 119 | "description": "A tour of migration options and tradeoffs for moving MongoDB workloads to Azure DocumentDB.", |
120 | 120 | "speakers": [{ "name": "Sandeep Nair", "slug": "sandeep-nair" }], |
121 | | - "url": "" |
| 121 | + "url": "https://youtu.be/OYtmeH0TSm4" |
122 | 122 | }, |
123 | 123 | { |
124 | 124 | "title": "Designing Cost-Efficient, High-Scale Systems with Azure Cosmos DB: Lessons from Production", |
|
142 | 142 | "title": "Designing High-Scale Event-Driven Microservices with Azure Cosmos DB", |
143 | 143 | "description": "Production patterns for building loosely coupled, event-driven microservices with Cosmos DB Change Feed as the domain event backbone.", |
144 | 144 | "speakers": [{ "name": "Tural Suleymani", "slug": "tural-suleymani" }], |
145 | | - "url": "" |
| 145 | + "url": "https://youtu.be/i4vUJSmFp9U" |
146 | 146 | }, |
147 | 147 | { |
148 | 148 | "title": "Memory for Your Agents: Building Chat History and Semantic Caching with Azure Cosmos DB", |
|
0 commit comments