6 Patterns of Cache

Cache Doesn't Make Your System Faster. It Just Moves the Complexity Somewhere Else.

There's a dangerous misconception in software engineering: that adding a cache is a performance solution. It isn't. It's a trade-off – one that swaps database load for a new set of problems around consistency, failure modes, and operational complexity.

Every caching decision ultimately comes down to one question: who is responsible for writing to the cache, and when does that write happen?

Answer that question differently and you get six distinct patterns. Each one makes sense in a specific context and falls apart in others. Here's a map of all six – not just what they are, but why you'd reach for each one.

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Data Engineering: ETL (Extract – Transform – Load) Overview

Understanding the backbone of every data-driven organization, a “starter kit” for anyone entering the world of Data Engineering

Modern organizations breathe data. Behind every dashboard, every metric, every “data-driven decision”, there’s a hidden machinery ensuring the right data flows in the right format to the right place. That machinery is ETL (Extract – Transform – Load), one of the foundational pillars of data engineering.

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Today I will expand those definitions into a cohesive, narrative-style guide to help you grasp ETL intuitively, and more understand how these concepts show up in real-world systems.

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