Most Data Engineering Advice is Wrong.
Here's what actually works in real-world systems.
I'm Guru, Lead Engineer
I build data systems that work, not systems that look good on slides.
❌ Bad Advice → ✅ What Actually Works
❌ "Use the latest tech stack"
Chasing trends, complex setups, vendor lock-in
✅ Use boring technology
PostgreSQL, Python, S3. Things that actually work.
❌ "Build perfect data models"
Months of modeling, over-engineered schemas
✅ Build good enough models
Start simple, evolve as needs change. Schema-on-read.
❌ "Real-time processing always"
Expensive, complex, often unnecessary
✅ Batch when you can
80% of use cases. Stream when you absolutely must.
❌ "Microservices for everything"
Distributed complexity, operational overhead
✅ Start simple, break apart later
Monolith first. Split when you feel pain.
🎯 3 Things That Actually Work Right Now
1. Start with CSV, not complex formats
Ship faster, get feedback, optimize later. I've seen teams spend 3 months on a "perfect" Parquet pipeline when CSV would have solved the business problem in 3 days.
2. SQL first, Python second
80% of data problems can be solved with SQL. It's faster, more maintainable, and your analysts can actually read it.
3. Manual beats automated for first 3 months
Understand the workflow before you automate. I've seen $500k automation projects for processes that took 2 hours per week.
What I Write About
- Real-world data engineering - not academic theory
- System design tradeoffs - why simple beats complex
- Tool evaluations - when to use what (and when not to)
- Lessons from production - what actually breaks and why
- Opinions on industry trends - separating hype from reality