Using Postgres as a Data Warehouse - Start with Postgres 18+ — asynchronous I/O makes table scans 2-3x faster than Postgres 15 - One command runs everything: `docker-compose up`. If partitioning breaks on localhost, it'll break in prod — test the real structure first - Async

Data Schema Is a Map, Not a Diagram - A schema isn’t tables and columns — it’s the story of how your data actually moves. Once you see the relationships, the whole system makes sense; until then it’s just names on a diagram. - Cardinality is the part everyone pretends to

The real question isn’t whether you can vibe-code Data Engineering— it’s whether you can debug it when it blows up, which is always sooner than you expect. And with distributed systems, vibe coding isn’t fun at all — real debugging skills beat LLM shortcuts every day.

Small teams are shipping enterprise products that used to require 50+ people. GitHub Copilot and Cursor aren't productivity tools — they're replacing 2-3 mid-level engineers per senior. Compensation gets simpler: revenue split by contribution, 20% retained for runway. Tech stack

The worst Data Engineering pipeline failures aren’t the dramatic outages with red dashboards and people yelling on Slack/Team. It’s the quiet ones — the jobs that keep “running” while doing absolutely nothing. Your Postgres sync drops from 40k rows/sec to 300 because VACUUM woke

In my day-to-day software engineering, Opus 4.5 hasn’t been very impressive. I still rely on Sonnet for coding work and OpenAI 5.1 for infrastructure.

Find the full code 👇 and run the data engineering observability stack https://t.co/70svEWX10Z

VictoriaMetrics for Data Engineering Pipeline Observability - Vectoriametrics uses far less RAM than Prometheus in official benchmarks and stores metrics for years without external storage hacks - vmagent is a small binary that scrapes metrics and pushes to VictoriaMetrics —

Code https://t.co/70svEWWtbr here

Why Ray for Data and AI Engineering: Building a distributed Python runtime used to mean months of infrastructure work. Ray turned it into an import statement. That's why Daft, Modin, and modern ML platforms use it as their execution layer instead of building custom schedulers.

The single most important Docker command here is: docker buildx build --platform linux/amd64 Skip the platform flag and you’re asking for painful deployment failures on your infra.

Code to start with ray https://t.co/70svEWX10Z here 👆

6 Things That Fixed My Slow Postgres Queries - EXPLAIN ANALYZE breaks under load — plans from my laptop meant nothing with 40 concurrent queries and hot buffers in production. I now capture plans during actual slowdowns, not in isolation. - I forced index hints on medium tables

Example code for the tech stack 👇 📷 https://t.co/70svEWX10Z here

Building Knowledge Search Without VectorDB using Nixiesearch - Documentation scattered across Confluence, GitHub, and Slack means engineers spend 20+ minutes grepping for "how we deploy the payment service" instead of just searching it - A lightweight hybrid search setup —

Life of a Data Infrastructure Engineer after 09:00 PM

📌 Find code https://t.co/70svEWX10Z here

Local Development Runs Your Data Stack Faster Than Cloud Staging Cloud staging environments are slow, expensive, and add friction to every iteration. Modern Local Data stack runs entirely on your laptop — full data pipelines, validation, and AI-powered debugging in under 60

Data testing is slow, painful, and rarely worth the effort at scale. Most devs skip to a quick sanity check and deploy — because with big data, full testing becomes nearly impossible and far too expensive. - Full dataset testing is mathematically impossible at scale — nobody's

There’s another big factor when it comes to funding in the EU: the amount of capital raised is usually far lower compared to similar startups in the US. Only a handful of EU-born companies — like n8n or Mistral (which, by the way, has massive EU backing) — are exceptions. For

In the last 3–4 years, tons of tech startups have popped up in the EU . But eventually, most of them shift their headquarters to the US. After working with a few of them in their early stages, I realized something very clear: if you want the right talent and real access to VCs,