3 posts tagged with "schema-as-code"

Anthropic recently published The AI-Native SDLC playbook, and it's the most concrete writeup I've seen discussing the effects of cheaper code generation on a delivery process, written by a company where AI authors 80% of merged code. If you run engineering at any scale, I suggest you read it.

It consists of six stages from Plan to Maintain that work in a loop rather than a linear chain of handoffs. Each stage commits an artifact that the next stage reads. The chain of commits is the audit trail. Humans stop reading every line and start judging intent and risk.

GitLab's CEO responded with the line that will likely outlive the playbook itself: "when implementation becomes abundant, trust becomes scarce."

Code is abundant now. Your data is not. An agent can regenerate your code in an afternoon, but it cannot regenerate the production state your company accumulated over years. The playbook's recovery model quietly assumes otherwise, which leads to our claim: the loop does not close at the database.

The database is the one stateful component in your infrastructure. It's where the history lives, and you cannot roll back history by redeploying an older version of it.

As of April 2026, MySQL 8.0 has reached End of Life (EOL), marking a critical security and operational milestone. When a database reaches EOL, the safety net is pulled away. Oracle will no longer release security patches, bug fixes, or performance improvements for the community edition. If a vulnerability is discovered tomorrow, your 8.0 instances will remain exposed.

In this post, we’ll explore what this means and how cloud platforms are responding to it.

Prepared for SREDay London 2025

Introduction​

Platform engineering is rapidly emerging as a discipline aimed at reducing cognitive load for developers, enabling self-service infrastructure, and establishing best practices for building and operating software at scale. While much of the conversation focuses on CI/CD, Kubernetes, and internal developer platforms, one crucial aspect often remains overlooked: database schema management.

Despite being at the heart of nearly every application, schema changes are still a major source of friction, outages, and bottlenecks. In this post, we'll explore why database schema management deserves a dedicated chapter in the platform engineering playbook and how organizations can integrate it into their platform strategies.

The prompt that nuked the database​

Let me tell you a not-so-fictional story about a developer named Alice. Alice is a backend engineer at a fast-growing startup. One day, her manager asked her to make a small change to the database. The data engineering team was complaining that they were seeing duplicate emails in the user table, and they suspected that the email column did not have a unique constraint.