Category: Data Science
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Beyond Simple RAG: How Context Engineering and GraphRAG Fix AI Performance
As Martin Keen from IBM explains, context is the single biggest bottleneck in getting AI to do what you want. While simple semantic search architectures helped us get started, building truly reliable, enterprise-grade AI systems requires shifting toward Context Engineering, Retrieval-Augmented Generation (RAG), and advanced GraphRAG models.
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10 Concepts EVERY Backend Dev Should Know
As a backend developer, your job is about much more than just writing code. You are the architect of the system’s “brain”—responsible for how data flows, how it’s stored, and how the system survives under pressure. Here are the 10 fundamental concepts that every backend developer must master to build production-grade systems in 2026. 1.…
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Why Netflix, Instagram, and Twitter Pick Different Databases
Choosing a database isn’t about finding the “best” technology; it’s about matching a database’s strengths to your specific access patterns. In a recent architectural deep dive, the channel ByteMonk explained why three of the world’s largest platforms chose fundamentally different database paths. [00:23] 1. Netflix: High-Write Throughput with Cassandra Netflix handles over 3 million writes…
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AI for Data Analysis in 21 Minutes: A Practical Guide to Smarter Analytics
AI For Data Analysis in 21 Minutes delivers a concise but powerful blueprint for integrating AI into everyday analytical work. From frameworks that help guide when to use AI, to practical examples with tools like Perplexity and ChatGPT, this video equips viewers to redefine traditional analysis workflows. Whether you’re in finance, marketing, research, or business…