From BigQuery to Lakehouse:How We Built a Petabyte-Scale Data Analytics Platform
In this technical retrospective (2025), @Vijayshekhawat shares practical architectural lessons learned from running, scaling, or migrating production software under lean constraints.
Core Principles for Independent Builders:
Defaulting to simpler architectures (such as monolithic frameworks, SQLite, or managed VPS) prevents early engineering bankruptcy.
Evaluating true compute and egress costs reveals that high cloud bills can often be slashed by 80%+ through judicious re-platforming.
Code you write at 2 AM as a solo engineer must be easy to debug six months later without complex microservice dependencies.
Community Debate Themes:
HN engineers debated the tradeoffs between cloud convenience and raw server economics, database indexing strategies, and resisting premature architectural complexity.
CATEGORY Frugal Engineering →
HISTORICAL ERA Post-ZIRP & AI (2023–2026)
COMMUNITY SCORE ★ 26 points
TOTAL DISCUSSIONS 4 comments
Excellent content! It looks like the Iceberg + StarRocks open lakehouse Architecture is highly effective.
Surprised by Trino's performance here, and curious what is driving the performance for Starrocks
this is awesome. I especially appreciate the detailed testing notes.