The ADE family
Modern analytics is fragmented: notebooks on one platform, semantic models on another, pipelines everywhere. Two open source products tackle the two questions every team keeps asking — what exists, and how do we operate it — and they are built to be driven by AI agents, not just read by humans.
ade-catalog
Cross-platform analytics metadata catalog
Maps your entire analytics landscape — what exists and how it's connected. It extracts metadata across platforms, builds cross-platform lineage that native tools don't provide, and exposes everything through a navigable web UI and an MCP server for AI agents. (Formerly ade-core.)
📊 Multi-platform metadata
Extracts and consolidates metadata from Databricks, Power BI, and Microsoft Fabric into a single queryable catalog.
🔗 Cross-platform lineage
Builds the data-flow graph across systems — Databricks → Fabric → Power BI — for real impact analysis, not platform-locked lineage.
🤖 MCP server for agents
Lets AI agents query the catalog, lineage, and impact directly — search, object detail, lineage, and stats over MCP.
🖥️ Web UI
A clean interface to browse and explore the catalog visually, with deployment both as a hosted service and on your own infrastructure.
ade-ops
Operations framework for multi-platform analytics teams
Most data-platform tools hand you a sample and walk away. ade-ops asks what you have, then scaffolds the workflow to operate it — keeping notebooks, semantic models, and reports in sync across dev, cert, and prod from a single source of truth.
📦 Single source of truth
One canonical src/ with overlay-based transforms for each environment — no hand-maintained per-environment copies.
🛡️ Diff before push
Remote workspaces are authoritative. Every promotion is visible — pull, diff, confirm, push. No silent overrides.
🧭 Scenario-aware onboarding
Asks about your setup — Databricks-only, Databricks → Power BI, or Databricks → Fabric → Power BI — and scaffolds what fits.
👥 Human + agent operation
Usable from a plain CLI, but reaches its potential paired with an AI coding assistant that drives its skills.
The YouTube series
The Autonomous Data Engineer is a video series documenting the journey of building an AI-based autonomous data engineer. Each episode explores a different aspect: from metadata extraction to autonomous pipeline navigation, from lineage tracking to code generation.
It's not a tutorial. It's a logbook — showing what works, what doesn't, and what happens when you give an AI agent real access to a data ecosystem.
The vision
Each analytics system has its own metadata, its own interface, its own language. ADE was born to close that gap — not with another dashboard, but with products an AI agent can drive: one that understands what exists across the stack, and one that operates it safely.
Together, ade-catalog and ade-ops form a continuous operational intelligence layer over an organization's analytics estate. Already in production with enterprise data teams.
The goal isn't to replace the data engineer. It's to give them a colleague that never sleeps, never forgets, and can traverse the entire stack in seconds.