Topic guide
Data Engineering
Data engineering is the work of moving, shaping, storing, and serving data so that downstream systems can rely on it. The difficult part is rarely moving bytes; it is defining ownership, correctness, failure recovery, and the point at which data becomes safe to use.
The practical questions
- Designing transformations and data pipelines
- Operating PostgreSQL indexes and schema changes safely
- Making search a product and ranking problem, not just a query
- Choosing storage and processing boundaries deliberately
Articles on Data Engineering
7 articles, ordered by topical relevance.
Indexes are not free
Adding an index speeds up reads. It also slows down writes, increases storage, and adds a maintenance burden that compounds over time.
postgres · database · architecture · software engineeringWhat a Data Pipeline Actually Is
ETL has three letters. Most people can explain two of them. The third is where teams make decisions that shape who owns data, when it is trustworthy, and how hard it is to change.
data · architecture · engineering · software engineeringSafe Database Migrations at Scale
A migration that passes in staging can lock a busy production table for minutes. The difference is not the migration. It is the assumptions behind it.
postgres · database · architecture · software engineeringWhy Search Is Such a Hard Thing to Talk About
Search sounds like one feature, but it is really a bundle of product decisions, customer expectations, matching strategies, ranking tradeoffs, and business context.
search · postgres · product engineering · software engineeringThe Cost of Making Everything Real Time
Real time sounds like an obvious improvement until you price it. The useful question is not how fast the data can arrive, it is how often anyone actually acts on it.
data · architecture · product engineering · software engineeringWhat Zero-Downtime Deployment Actually Requires
Zero-downtime deployment depends on overlapping versions, safe traffic shifts, and database changes that work before and after release.
devops · architecture · database · software engineeringAI Tools Are Not Magic Bullets
LLMs are not magic bullets. They are powerful translation engines for messy data, especially when you use them to summarize, structure, and move information between systems.
ai · llms · data · software engineering · automation