\n
![\"Excel](\"https:\/\/www.newsbeep.com\/uk\/wp-content\/uploads\/2026\/07\/excel-on-ipad-pro.jpg\"\/)
<\/p>\n<\/a><\/p>\n
Related<\/p>\n
\t\t Sifting through data just got easier with this quick and easy Excel formula. <\/p>\n Spreadsheets quietly became my database<\/p>\n Years of growth exposed Excel’s limits<\/p>\n Nobody plans to outgrow a spreadsheet. You don’t wake up one day and decide it’s time to stop using Excel and switch to something else<\/a>. It happens gradually \u2014 a single spreadsheet becomes three tabs, then ten, then a master file referencing half a dozen others. Formulas start pointing at cells that got deleted years ago. Then someone fat-fingers a filter, and half your rows vanish, and you don’t even realize that until it’s time to get something useful out of the mess of data you call a database.<\/p>\n The real problem isn’t the errors themselves; it’s that Excel’s safeguards are all optional. You can go the route of replacing error-checking spreadsheets with Excel data validation<\/a> to catch mistakes before they happen, and Power Pivot can even model relationships between sheets, but almost nobody sets these up, and nothing stops you from overriding them later. There’s no enforced relationship the way a foreign key enforces one between tables. Every safeguard is a manual habit, and habits break under deadline pressure more often than you’d like.<\/p>\n Postgres solved problems Excel never could<\/p>\n Built for structured, reliable data<\/p>\n My breaking point came when I realized I needed to cross-reference three spreadsheets to answer a question that should have taken 30 seconds. Instead, it took an afternoon of VLOOKUPs and copy-pasting, and I still wasn’t confident in the answer. That frustration finally pushed me to figure out a database solution, and Postgres was the perfect fit. It’s easy to set up, runs as a containerized service, and is easily pluggable into local LLMs for AI analysis.<\/p>\n Given my usual habits of running things in Docker \u2014 these six Docker containers made my home server worth it long before this \u2014 spinning up Postgres wasn’t a huge leap. You just need the basics, and a Docker Compose file with a named volume for persistent storage had it running in under 10 minutes. I also had prior experience working with Postgres when I fixed Claude’s memory issues by giving it a Postgres database<\/a>.<\/p>\n One weekend was all it took<\/p>\n The migration was easier than expected<\/p>\n The migration process took some planning, but it was easy enough to do. I started with my messiest spreadsheet, a client and order tracker held together with formulas, and sketched out the actual tables: customers, orders, payments. That exercise alone, thinking in normalized tables instead of flat rows, exposed how much duplicate and inconsistent data I’d been carrying around for years. Then I wrote a small Python script using pandas to read the old file, clean it up, and insert everything into Postgres with psycopg, the most popular Postgres adapter for Python.<\/p>\n This put the schema in place with foreign keys and constraints. I spent the next day watching those constraints do the work I used to do by hand. A company was in the sheet twice under the same email, re-entered months later with a trailing space in the name that made it look new; the unique constraint on email refused the duplicate instead of letting a second copy sit there. One order had no date at all, and the non-null constraint kicked it back instead of letting it pollute my data ( I did have to track down the original entry and fix it).<\/p>\n Another referenced a customer email that didn’t exist on the list at all, the kind of stray entry a VLOOKUP would happily return a blank for. Postgres’s foreign key rejected it outright. One payment had “paid in full” typed into the amount column instead of a number, which Postgres refused to store as currency until I fixed it.<\/p>\n Not every messy column turned into a dramatic catch, though. My invoice column had yes, no, true, and N scattered across it, and Postgres’s boolean type parsed almost all of it happily. Constraints stop bad data, not bad formatting.<\/p>\n Everything became easier to manage<\/p>\n Cleaner data, faster queries, fewer mistakes<\/p>\n The difference isn’t just speed, although that’s a major benefit. The client-invoicing question that used to take an afternoon in Excel is now a five-line SQL query, and my Flash dashboard shows the answer the moment the page loads. I no longer wonder if a formula got dragged into the wrong cell or someone overwrote a value. The database enforces rules I used to enforce through memory and vigilance. When it can’t, it fails loudly and forces me to fix the problem before it snowballs.<\/p>\n Another major advantage is that I can literally talk to my data now, courtesy of a couple of local LLMs and Claude all having access to the same database. So for the most part, I don’t even have to write SQL. Simply asking my AI agents to find me a piece of data works well enough. Backups also run on schedule now, instead of me hoping I remembered to save a versioned copy before making permanent changes.<\/p>\n Excel still has a place<\/p>\n Great for analysis, not long-term storage<\/p>\n Excel isn’t useless, and I still use it for sharing data with people who aren’t going to run SQL queries. But as a system of record, it was never built for the job I was asking it to do.<\/p>\n
\n\t\t\tThis Excel function eliminated hours of manual text combining
\n\t\t<\/a><\/p>\n![\"DBweaver](\"https:\/\/www.newsbeep.com\/uk\/wp-content\/uploads\/2026\/07\/dbweaver-showing-postgres-database.jpg\")
\n Screenshot by Yadullah Abidi | No Attribution Required.<\/p>\n![\"Postgres](\"https:\/\/www.newsbeep.com\/uk\/wp-content\/uploads\/2026\/07\/postgres-dashboard.jpg\")
\n Screenshot by Yadullah Abidi | No Attribution Required.<\/p>\n
![\"Gemma](\"https:\/\/www.newsbeep.com\/uk\/wp-content\/uploads\/2026\/07\/gemma-4-responding-to-a-prompt-on-a-macbook-in-lm-studio.jpeg\"\/)
<\/p>\n