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Data Engineering

Data Mesh: Moving Beyond the Centralized Data Lake

In the early days of big data, the centralized data warehouse was the gold standard. It worked beautifully for small-to-medium scale analytics. However, as organizations grew, these centralized architectures began to crumble under the weight of scalability issues, slow time-to-insight, and lack o...

System Design

Building Scalable Recommendation Systems: A Technical Deep Dive

Recommendation engines are the backbone of modern digital experiences, driving engagement for platforms like Netflix, Spotify, and Amazon. However, designing a system that can process billions of interactions in real-time while maintaining relevance is a complex engineering challenge. This post e...

Evaluation

Scalable Human Evaluation: Mitigating LLM Bias

As Large Language Models (LLMs) become central to enterprise workflows, the need for rigorous human evaluation grows. Automated metrics like perplexity or BLEU often fail to capture nuance, toxicity, or factual accuracy. Human-in-the-Loop (HITL) evaluation provides the ground truth, but scaling i...