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title: Best ByteByteGo posts — November 2025 | daily.dev
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og:description: The most upvoted ByteByteGo posts from November 2025, curated by the daily.dev community.
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# Best of ByteByteGo — November 2025

1. 1  
[](https://daily.dev/posts/how-disney-hotstar-now-jiohotstar-scaled-its-infra-for-60-million-concurrent-users-iubf4kue1 "How Disney Hotstar (now JioHotstar) Scaled Its Infra for 60 Million Concurrent Users")  
Article  
![Avatar of bytebytego](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/35be29234ee14d01a9cd049c52e12753)ByteByteGo·42w  
How Disney Hotstar (now JioHotstar) Scaled Its Infra for 60 Million Concurrent Users  
Disney+ Hotstar scaled from 25 million to 61 million concurrent users during the 2023 Cricket World Cup through a comprehensive infrastructure overhaul. Key improvements included separating cacheable from non-cacheable APIs at the CDN layer, migrating from self-managed KOPS to Amazon EKS, implementing distributed NAT gateways per subnet, and introducing a Datacenter Abstraction model. This abstraction unified multiple Kubernetes clusters into logical data centers with a centralized Envoy-based API gateway, replacing 200+ individual load balancers. The team also eliminated NodePort limitations by switching to ClusterIP services, standardized service endpoints, and adopted single-manifest deployments. The final architecture distributed 200+ microservices across six optimized EKS clusters, each designed for specific workload types.  
117  
1
2. 2  
[](https://daily.dev/posts/how-spotify-built-its-data-platform-to-understand-1-4-trillion-data-points-796evwtpl "How Spotify Built Its Data Platform To Understand 1.4 Trillion Data Points")  
Article  
![Avatar of bytebytego](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/35be29234ee14d01a9cd049c52e12753)ByteByteGo·43w  
How Spotify Built Its Data Platform To Understand 1.4 Trillion Data Points  
Spotify processes 1.4 trillion data points daily through a sophisticated data platform that evolved from a single Hadoop cluster to a multi-product system running on Google Cloud. The platform consists of three core components: data collection (capturing events from millions of devices using client SDKs and Kubernetes operators), data processing (running 38,000+ automated pipelines using BigQuery, Flink, and Apache Beam), and data management (ensuring privacy, security, and compliance). The architecture emphasizes self-service capabilities, allowing product teams to define event schemas and deploy infrastructure automatically while maintaining centralized governance. Built-in anonymization, lineage tracking, and quality checks ensure data trustworthiness across financial reporting, personalized recommendations, and experimentation systems.  
67
3. 3  
[](https://daily.dev/posts/ep189-how-to-design-good-apis-byuckuzfn "EP189: How to Design Good APIs")  
Article  
![Avatar of bytebytego](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/35be29234ee14d01a9cd049c52e12753)ByteByteGo·42w  
EP189: How to Design Good APIs  
Covers fundamental principles of API design including idempotency, versioning, resource naming, security, and pagination. Explores big data pipeline architectures across AWS, Azure, and GCP. Provides a structured learning path for AWS services from fundamentals through certifications. Explains RAG application architecture on AWS and compares virtualization approaches from bare metal to containers on VMs.  
53
4. 4  
[](https://daily.dev/posts/how-uber-built-a-conversational-ai-agent-for-financial-analysis-m8kmppmah "How Uber Built a Conversational AI Agent For Financial Analysis")  
Article  
![Avatar of bytebytego](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/35be29234ee14d01a9cd049c52e12753)ByteByteGo·43w  
How Uber Built a Conversational AI Agent For Financial Analysis  
Uber built Finch, a conversational AI agent that enables finance teams to query financial data using natural language directly in Slack. The system translates questions into SQL queries, retrieves data from curated single-table data marts, and returns results in seconds. Finch uses a modular architecture with specialized agents orchestrated by LangGraph, OpenSearch for semantic mapping, and role-based access controls for security. The system includes continuous evaluation against golden queries, performance optimizations through parallel processing and pre-fetching, and plans to expand with deeper FinTech integration and human-in-the-loop validation for executive decisions.  
52

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