OpenAI’s data productivity team built Kepler, an AI agent that understands their data platform context and writes iterative SQL to answer complex data questions. It handles table discovery, query refinement, follow-up prompts and even visualizations, cutting analysis time and reducing errors.

This issue covers a new AI-built SQL parser for PostHog that’s 70× faster, Linear’s move from styled-components to StyleX for better React performance, and a deep dive into misleading database benchmarks. It also highlights tools like Gemini 3.5 Flash’s computer use feature, the peerd automation extension, a breakdown of prompt injection vulnerabilities, and other AI and dev updates.

This tool parses your SQL CREATE TABLE and ALTER TABLE statements and instantly renders an interactive ER diagram in your browser. It works entirely locally—no uploads or accounts—supports PostgreSQL, MySQL, SQLite, and SQL Server, and lets you drag tables, rename elements, and export to PNG or SVG.

ggsql extends SQL so you can write data queries and visualization specs in one composable syntax based on the Grammar of Graphics. It compiles to WebAssembly, works with DuckDB/SQLite and Vega-Lite, and offers an in-browser playground for immediate testing.

PgQue is a pure SQL/PLpgSQL event queue for Postgres 14+ that delivers durable, Kafka-style streams without dead tuples or external daemons. It uses snapshot-based batching and TRUNCATE-driven table rotation for stable performance under sustained load. A pg_cron (or any scheduler) ticker drives 1–2 second end-to-end delivery.

Rill’s Metrics SQL lets you define business metrics once and query them using plain SQL across dashboards, notebooks, and AI agents. It compiles metric views into optimized OLAP queries, handling grouping, filters, time functions, and security automatically.

The author shares their shift from using Excel and Google Sheets to DuckDB and SQL for handling CSV files, highlighting the efficiency of querying data directly. They discuss the benefits of using SQL for data manipulation and invite readers to share their own CSV handling tips.

Since the inception of SQL in 1974, there has been a recurring dream to replace data analytics developers with tools that simplify the querying process. Each decade has seen innovations that aim to democratize data access, yet the complex intellectual work of understanding business needs and making informed decisions remains essential. Advances like AI can enhance efficiency but do not eliminate the crucial human expertise required in data analytics.

chDB transforms ClickHouse into a user-friendly Python library for seamless DataFrame operations, eliminating serialization overhead and enabling fast SQL queries directly on Pandas DataFrames. The latest version achieves significant performance improvements, making it 87 times faster than its predecessor by implementing zero-copy data handling and optimized processing.