Atlan: The Ai Context Layer For Enterprise Data
Published 7/2026
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Language: English |
Duration: 54m |
Size: 112.24 MB
Learn Atlan's context layer - data catalog, lineage, governance, Context Agents, and activating metadata for AI via MCP.
What you'll learn
Understand why AI needs a context layer: definitions, lineage, and policy
Know what Atlan is and the unify → generate → certify → activate flow
Connect your data stack with native connectors (80+ systems)
Grasp active metadata and why it keeps AI accurate
Find and judge data with discovery, asset profiles, and trust signals
Build a governed business glossary linked to real assets
Use column-level lineage for impact analysis and root-cause debugging
Scale documentation with Context Agents (generate, then certify)
Govern with policies, access control, and classifications that flow with lineage
Surface data quality and trust signals for humans and AI
Activate certified context for AI agents via MCP, SQL, and APIs
Understand the Metadata Lakehouse architecture, roll out successfully, and measure value
Requirements
Basic familiarity with data concepts (tables, warehouses, dashboards
No Atlan experience required - this course starts from zero
Description
This course contains the use of artificial intelligence.
AI agents and analytics are only as good as the context behind the data - the business definitions behind column names, the lineage behind every output, and the policies behind every query. Atlan is the platform built to provide that context layer, and it's now a Leader in every major analyst evaluation for metadata and governance. This course teaches you Atlan from the ground up.
You'll start with why context is the missing tier between data and AI, what Atlan is, and the active-metadata approach that keeps context accurate automatically. Then you'll learn the core platform: connecting your systems, data discovery and cataloging, the business glossary, and column-level data lineage.
Next comes automation and governance: Context Agents that auto-generate descriptions and definitions for human certification, governance policies and access control, data quality and trust signals, and collaboration workflows. The final part covers what makes Atlan an AI context layer - activating certified context for AI agents via MCP, SQL, and APIs, the Metadata Lakehouse architecture underneath, and how to roll Atlan out and measure its value - with a capstone that brings it all together.
By the end you'll understand how to build a trusted context layer on Atlan, for both your human data teams and the AI agents that increasingly depend on it.
Who this course is for
Data leaders, stewards, and governance teams evaluating or adopting Atlan
Data engineers and analytics engineers who own data platforms
Analysts and data product managers who need trusted, findable data
AI/platform teams grounding agents in enterprise data context
Atlan The Ai Context Layer For Enterprise Data
