gulcin_xata
A batch size that works well in a low latency environment can become a bottleneck when snapshots run across regions or under less predictable network conditions. Static batch size configuration assumes stable networks, which rarely reflects reality.
In this blog post we describe how we used automatic batch size tuning to optimize data throughput for Postgres snapshots, the constraints we worked under and how we validated that the approach actually improves performance in production-like environments for our open source pgstream tool.
From DBA to AI Agent: Lessons from 10 Years with Postgres
(Disclaimer: I work at Xata.) Just wanted to mention that we also support anonymization, in case that’s something you're looking into: https://xata.io/postgres-data-masking
Writing an LLM Eval with Vercel's AI SDK and Vitest
To make sure that Xata Agent still works well after modifying a prompt or switching LLM models we decided to test it with an Eval. In this blog, we'll explain how we used Vercel's AI SDK and Vitest to build an Eval in TypeScript.
Have you seen pgroll? https://github.com/xataio/pgroll It is a Postgres schema migration tool for achieving zero downtime, minimal locking schema changes. Thought it might be interesting for you. I also checked the unsupported operations in pg-schema-diff, and from a quick look, pgroll seems to cover more migration types: https://pgroll.com/docs/v0.8.0/getting-started