For years, the default performance strategy in enterprise IT looked something like this:👉 “It’s slow? Buy a bigger server!”👉 “Still slow? Buy an even bigger one!” I’ve seen this firsthand in a large enterprise environment that relied on a single Oracle DB instance – no horizontal scaling, no real way to distribute load. When performance […]

AI is writing more code than ever.Faster commits. Fewer bugs. Shorter sprints. And it never argues in code reviews! Perfect teammate, right? Until you realize it also never maintains its code, never fixes production bugs at 2 a.m. and definitely never sits in post-mortems explaining why it hallucinated a new API endpoint. Here’s the catch

In large-scale IT systems, especially those processing billions of raw data records, even a tiny error rate can translate into millions of issues – potentially affecting reporting, billing, compliance, or even revenue recognition. This raises a critical question:How do you handle data processing errors at scale? Some organizations rely heavily on manual validation. It works…

Recently, one of our friends started a freelance project. A small system, some backend elements, e-commerce, and variety of external APIs. Simple stuff, right? With plenty of time before the deadline, he thought:“Perfect chance to try this cool new framework everyone’s talking about!  Why not let an AI assistant help me code?” In the beginning,

It starts harmlessly enough:◽ Postgres for core data 💪◽ Redis for caching ⚡◽ Elasticsearch because “search is hard” 🤓◽ DynamoDB… no one remembers why, but it’s in prod now 😬◽ ClickHouse for analytics because someone read a blog post about it once 📊◽ SQLite for that one internal CLI tool, obviously 💡◽ Oh, and MongoDB

If one cloud can go down, just use two clouds, right? That’s resilience. Or… is it just a fancy way to double your AWS and Azure bills while increasing the number of dashboards you ignore? We’ve all heard it: Suddenly, you’re knee-deep in IAM policies from two different providers, debugging why your GCP function can’t

You set up monitoring to get peace of mind…Now you’re managing 6 dashboards, 3 alerting systems, and a Slack channel that never sleeps. Prometheus. Grafana. Loki. Datadog. ELK. Sentry. PagerDuty… Somehow, we went from “just add a metrics exporter” to needing a PhD to figure out why latency spiked at 3am. We just wanted observability…

You’re not alone. Many teams struggle to find a simple, effective solution that meets their unique needs. Too often, you’re forced to choose between: 🔹 Tools that are too simple and lack even basic functionality 🔹 Tools that are too complex, expensive, and bloated with features you’ll never use 🔹 Multiple disconnected apps that don’t