MDV vs Rocky

MDV (Markdown Data Views) is a markdown superset that extends standard .md files with embedded live data, interactive charts, and presentation-ready slides. The goal is a single document format that serves simultaneously as developer documentation, a live dashboard, and a shareable slide deck — without requiring a separate tool for each use case. MDV files can embed SQL queries, API calls, and data transforms directly in markdown, with results rendering as tables, charts, or visualizations on the fly. The syntax extends frontmatter conventions that markdown users already know, keeping the learning curve minimal. Output can be previewed in a local server, exported as HTML, or converted to a slide deck — the same source file serves all three outputs. MDV surfaced on Hacker News with 44 points and active discussion around the concept of "living documents" — reports and runbooks that stay current because their data sources are live queries rather than screenshots. For developer-heavy teams who live in their editors and resist adopting heavyweight BI tools, MDV offers a markdown-native alternative that slots into existing documentation workflows.

Rocky is a Rust-based SQL transformation engine that brings software engineering discipline to data pipelines. Where tools like dbt gave data teams a version-controlled workflow, Rocky goes further: type-safe compile-time SQL, column-level lineage visualization, git-style branches for isolated testing, and a built-in AI intent layer that stores your purpose as metadata alongside the code. The branching feature is the standout — you can create a branch, run it against an isolated schema, inspect the results, then drop or promote. The column-level lineage shows the full downstream blast radius before you ship a change, tracing any single column back through every aggregation and join to its source. This is the kind of visibility that prevents the "who broke the revenue dashboard" post-mortems that happen in every data team. The AI intent layer is genuinely novel: it stores what a model is supposed to do as metadata, so AI can later explain models, auto-update them when upstream schemas change, and generate tests based on the original intent. Rocky integrates with Dagster via an official plugin and supports DuckDB for local development with no credentials required. With Hacker News coverage and a Rust-native architecture, it's positioned as the data pipeline tool for engineering-forward teams who are tired of YAML-based transformations.