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1 — Netflix Play API - An Evolutionary Architecture
2 — Designing Services for Resilience: Netflix Lessons
3 — Microservices: Patterns and Practices Panel
4 — Digital Assets: Lessons in Securing What’s Next
5 — Why We Chose Erlang over Java, Scala, Go, C
6 — Scaling Event Sourcing for Netflix Downloads
7 — Real-Time & Personalized Notifications @Twitter
8 — DDD and Microservices: At Last, Some Boundaries!
9 — Cloud-Native and Scalable Kafka Architecture
10 — Chaos Engineering: Why the World Needs More Resilient Systems
11 — Serving Millions of Customers Serverless at CapitalOne
12 — Using Chaos to Build Resilient Systems
13 — Reactive DDD—When Concurrent Waxes Fluent
14 — From Winning the Microservice War to Keeping the Peace
15 — An Engineer's Guide to a Good Night's Sleep
16 — No Moore Left to Give: Enterprise Computing after Moore's Law
17 — Microservices for Growth at comparethemarket.com
18 — Controlled Chaos: Taming Organic, Federated Growth of Microservices
19 — Evolution of Financial Exchange Architectures
20 — Introducing and Scaling a GraphQL BFF
21 — Why Distributed Systems Are Hard
22 — The Not-So-Straightforward Road from Microservices to Serverless
23 — Streaming a Million Likes/Second: Real-Time Interactions on Live Video
24 — Moving beyond Request-Reply: How Smart APIs Are Different
25 — To Microservices and Back Again
26 — Cloudstate—towards Stateful Serverless
27 — From Batch to Streaming to Both
28 — Modern Banking in 1500 Microservices
29 — Kafka: A Modern Distributed System
30 — Monolith Decomposition Patterns
31 — Rampant Pragmatism: Growth and Change at Starling Bank
32 — Panel: Microservices - Are They Still Worth It?
33 — A Brief History of the Future of the API
34 — Tesla Virtual Power Plant
35 — Self-Driving Cars as Edge Computing Devices
36 — Service Mesh Ultimate Guide: Managing Service-to-Service Communications in the Era of Microservices
37 — User & Device Identity for Microservices @ Netflix Scale
38 — Architectures That Scale Deep - Regaining Control in Deep Systems
39 — Build Your Own WebAssembly Compiler
40 — Parsing JSON Really Quickly: Lessons Learned
41 — Beyond Microservices: Streams, State and Scalability
42 — Building a High-Performance Networking Protocol for Microservices
43 — PID Loops and the Art of Keeping Systems Stable
44 — Peloton - Uber's Webscale Unified Scheduler on Mesos & Kubernetes
45 — Practical DDD: Bounded Contexts + Events = Microservices
46 — Scaling Infrastructure Engineering at Slack
47 — Interaction Protocols in Software: It's All about Good Manners
48 — Continuous Profiling in Production: What, Why and How
49 — Reactive Systems Architecture
50 — Performance: What's Next?
51 — Crisis to Calm: Story of Data Validation @ Netflix
52 — Mature Microservices and How to Operate Them
53 — Restoring Confidence in Microservices: Tracing That's More Than Traces
54 — Connecting, Managing, Observing, and Securing Services
55 — Patterns of Streaming Applications
56 — Paying Technical Debt at Scale - Migrations @Stripe
57 — CRDTs in Production
58 — The Great Migration: from Monolith to Service-Oriented
59 — What We Got Wrong: Lessons from the Birth of Microservices
60 — No Microservice Is an Island
61 — Lyft's Envoy: Embracing a Service Mesh
62 — Serverless + Containers = Modern Cloud Applications
63 — Complex Event Flows in Distributed Systems
64 — Serverless Patterns and Anti-patterns
65 — Designing Events-First Microservices
66 — Scaling Push Messaging for Millions of Devices @Netflix
67 — Actors or Not: Async Event Architectures
68 — How to Build Observable Distributed Systems
69 — The Present and Future of Serverless Observability
70 — Applied Performance Theory
71 — Insecure Transit - Microservice Security
72 — How Events Are Reshaping Modern Systems
73 — Microservices Lessons Learned from a Startup
74 — Avoiding Alerts Overload from Microservices
75 — Chaos Architecture
76 — Scaling Facebook Live Videos to a Billion Users
77 — Mastering Chaos - A Netflix Guide to Microservices
78 — Managing Data in Microservices
79 — Building a Bank with Go
80 — Spotify's Reliable Event Delivery System
81 — Presidential Campaigns & Immutable Infrastructure
82 — Applying Failure Testing Research @Netflix
83 — From Concurrent to Parallel
84 — What Comes after Microservices?
85 — In-Memory Caching: Curb Tail Latency with Pelikan
86 — From Microliths to Microsystems
87 — Functional / Microservices in Real-Time Financials
88 — Distributed Systems Theory for Practical Engineers
89 — How Slack Works
90 — What do you know about testing in production?
91 — Take Two: Evolving Microservice Architectures
92 — Scaling Slack
93 — Architecting a Modern Financial Institution
94 — Service-Oriented Development
95 — Data Consistency in Microservice Using Sagas
96 — Istio - Weaving the Service Mesh
97 — The Evolution of Reddit.com's Architecture
98 — The Anatomy of a Distributed System
99 — The Future of Artificial Intelligence
100 — Testing in Production - Quality Software Faster
101 — Opportunities and Pitfalls of Event-driven Utopia
102 — Scaling Patterns for Netflix's Edge
103 — Managing Failure Modes in Microservice Architectures
104 — Beyond the Distributed Monolith: Rearchitecting the Big Data Platform
105 — Designing Secure Architectures the Modern Way, Regardless of Stack
106 — Reconciling Performance and Security in High Load Environments
107 — Lessons from DAZN: Scaling Your Project with Micro-Frontends
108 — Infinite Parallel Universes: State at the Edge
109 — The Correct Number of Microservices for a System Is 489 (Panel)
110 — Complex Event Flows in Distributed Systems
111 — CRDTs and the Quest for Distributed Consistency
112 — Design Microservice Architectures the Right Way
113 — Building Tech at Presidential Scale
114 — Evolution of Edge @Netflix
115 — Architecting for the Edge
116 — Present and Future of the Microservice Architecture
117 — Netflix Drive: Building a Cloud Native Filesystem for Media Assets
118 — Managing Tech Debt in a Microservice Architecture
119 — Event-Based Architectures: the Hard Parts
120 — The Top Five Challenges of Running a Service Mesh in an Enterprise
121 — Airbnb at Scale
122 — Seven Ways to Fail at Microservices
123 — Data Mesh in the Real World: Lessons Learnt from the Financial Markets
124 — Co-Designing Raft + Thread-per-Core Execution Model for the Kafka-API
125 — Architecting Software for Leverage
126 — Resources & Transactions: a Fundamental Duality in Observability
127 — Building and Scaling a Control Plane for 1000s of Kafka Clusters
128 — Panel: What Have We Learned over the Last Decade of Microservices?
129 — Complex Systems: Microservices and Humans
130 — Unwinding a Decade of Assumptions - Architecting New Experiences
131 — Panel: Event Driven Architectures of Scale
132 — User Adaptive Security
133 — Pitfalls and Patterns in Microservice Dependency Management
134 — Minimizing Design Time Coupling in a Microservice Architecture
135 — Essential Complexity in Systems Architecture
136 — Architecting for Focus, Flow, and Joy: beyond the Unicorn Project
137 — Pragmatic Performance - Tales from the Trenches
138 — The World Is on Fire and so Is Your Website
139 — User Simulation for Rapid Outage Mitigation
140 — Change Data Capture for Distributed Databases @Netflix
141 — Rebuilding Twitter’s Public API
142 — Solving Mysteries Faster with Observability
143 — How to Tame Your Service APIs: Evolving Airbnb’s Architecture
144 — From Monolith to Microservices
145 — It’s Not Your Machine, It’s Your Code
146 — The Medieval Census Problem
147 — Building Latency Sensitive User Facing Analytics via Apache Pinot
148 — How Netflix Scales Its API with GraphQL Federation
149 — The Common Pitfalls of Cloud Native Software Supply Chains
