Every story tagged Operational Best Practices, curated for CIOs and IT leaders — ranked by source credibility, engagement, and freshness.
2 stories · open in the command center
Software teams are among the most capital-intensive business investments (€87K/month for 8 engineers), yet most organizations lack financial visibility into what teams cost or the value they must generate—typically 3-5x their costs to account for failed initiatives and long-term maintenance burden. This financial blindness affects daily prioritization decisions, with teams often pursuing interesting work rather than high-value problems that justify their existence. The rise of AI/LLMs will expose this structural issue as productivity gains reduce the number of engineers needed, forcing organizations to finally confront whether their engineering investments deliver adequate returns.
Using Postgres as a job queue alongside mixed workloads creates operational risk when database cleanup (vacuum) cannot keep pace with high-volume transient data, potentially degrading performance across the entire database and impacting all applications. While Postgres can technically handle queue workloads at scale, the challenge lies in maintaining queue health when competing with OLTP, OLAP, and other workload types sharing the same database resources. Dead tuple accumulation from constant insert-delete operations can cause table bloat that affects not just the queue but all database operations if vacuum processes fall behind.