Weekly tech digest: CAP theorem basics, Uber’s probabilistic heatmaps, Dropbox’s context-aware AI, and Lyft’s modern ML platform architecture.

An article with a comparison between the dot-com bubble and today’s AI boom, highlighting the hype, the parallels, the fundamental differences, and why some AI companies will thrive while others will vanish during the inevitable market correction.

In this article, we explore practical patterns for data versioning and schema evolution in NoSQL systems.

This week: MongoDB best practices, Netflix’s ML platform, Uber’s I/O observability at petabyte scale, and Google’s Coral NPU for edge AI.

 Walk through consistency models in distributed systems: Strong, bounded staleness, session, causal, and eventual consistency, explain how they work with examples, and help you understand when each model makes sense.

In this article, we explain the CAP theorem in simple terms: what Consistency, Availability, and Partition Tolerance mean, why you can’t have all three, and how real systems balance them in practice.

This week, we dive into SQL performance tips, Uber’s approach to fault-tolerant analytics, and how TikTok cut AI costs in half with smart caching.

Practical MongoDB best practices guide for developers. Covers schema design, indexing, performance, queries, transactions, sharding, and common mistakes with examples and links to official docs.

This week: SQL interview prep for .NET engineers, Geocodio’s geocoding scalability journey, Netflix’s real-time live recommendations, and Airbnb’s LLM-powered GraphQL mocking.

Practical SQL best practices for developers and DBAs. Learn how to design clean schemas, write readable and efficient queries, avoid common mistakes, and keep your database secure and fast

This chapter explores SQL Database questions that  .NET engineers should be able to answer in an interview.

Weekly tech digest: pagination strategies for large datasets, Uber’s rebuilt Apache Pinot query architecture, and Meta’s ML-based notification ranking system for Instagram.