Michael Ikechukwu retweetledi
Something most developers don’t think about…
OLTP and OLAP both use SQL and query tables, but they solve completely different problems.
OLTP powers your app: small reads, fast writes, low latency. Users create records, update data, and interact in real time.
OLAP serves your analyst: scanning millions of rows and crunching a few columns. Questions like “What was total revenue across all stores last quarter?” are what OLAP is optimized for.
Running both on the same database kills production performance with one heavy report query.
That’s why data warehouses exist: pull data from production, transform it, and load it into a system built for analytics (ETL).
Same SQL. Completely different beasts underneath.
chinedu🦀@chinedu_10
Talking about databases... Databases are basically an append-only log with extra steps. Writes are just appending to the end. Makes it blazing fast, no random disk seeks. Reads? Without help, you end up scanning the whole file, which results in a painful O(n). So the solution is to add an index a hash map (or something similar) from key to byte offset on disk. Now reads become O(1). But there’s a tradeoff: every index costs you write speed. That’s why databases don’t index everything by default you choose based on your actual queries. Storage engines aren’t magic. They’re just data structures brutally optimizing the read/write/amplification trade-offs.
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