🚨Day 7 of 30 Days #BuildingInPublic as an AgriFood Data Analyst. This is the start of my second SQL Analytics Project powered by @DeepnoteHQ. Meet Mkulima Direct FoodMart. It operates in three outlets — Nairobi, Nakuru, Meru — on busy roads, serving working professionals who order mostly on WhatsApp, sometimes online, sometimes walk in. Nakuru and Meru sit in real agricultural zones, so they procure produce, cereals, and value-added goods locally and ship the surplus north. Nairobi has no farmland of its own — it sells the same catalog, sources meat and poultry directly from urban distributors, and leans on transfers from the other two for everything else. I’ve been brought in to answer one question management can’t: is this business actually profitable once every cost is counted, not just the obvious ones? First, I built the data to make that answerable. Every single purchase is its own tracked batch — not a running stock number, an individual event with its own cost, its own supplier, its own fate. From there, it can go one of six ways: sold, rejected on arrival, spoiled in storage before it ever leaves the source outlet, lost in transit on the way to Nairobi, spoiled again after arrival, or left to expire unsold on the shelf. The data includes 14 tables, including over 2,900 batches, 47,700 orders, 82,700 ordered items, and a waste log of 13,900 items. A full year (Jan – Dec 2025) with every kilo accounted for, one way or another. Here’s the uncomfortable part I already found before writing a single line of SQL: total revenue for the year barely clears total cost. And a meaningful chunk of everything purchased never made it to a customer at all. The Question: Is that a pricing problem, a logistics problem, or a waste problem? That’s what the next several days are for. Building the schema, running the joins, and letting the data argue with itself.