DataHub

DataHub User Training

Pick the learning path that sounds like you. Each path lists just the modules you need, tracks how far you've gotten, shows your exam scores, and awards a certificate when you pass every module exam.

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👤

The Data Consumer

You're an analyst, BI developer, data scientist, or business stakeholder who needs to find data and trust it — not learn the plumbing.

6 modules · ~2h 25m

Not started
📤

The Data Publisher

You produce and publish datasets for others to consume — you don't own the domain, but you're responsible for making your data findable and understandable.

5 modules · ~1h 45m

Not started
🔧

The Data Engineer / Platform Builder

You build and run pipelines (internal platforms, Snowflake, Airflow, dbt) and are on call when they fail.

6 modules · ~2h 30m

Not started
🤖

The AI/ML Builder

You ship ML/AI models into production and need provenance and certification for the data they depend on.

3 modules · ~1h 10m

Not started
🧑‍💼

The Data Steward / Domain Owner

You write definitions, assign ownership, tag assets, and maintain the glossary for your domain, day to day.

11 modules · ~4h 15m

Not started
🏛️

The Governance Champion

You own or are standing up a data governance program and are turning a leadership mandate into a rollout.

6 modules · ~2h 05m

Not started
💰

The Executive Sponsor (CDO / VP)

You're the CDO or VP who sponsors the initiative and answers for outcomes — posture over click-paths.

3 modules · ~1h 10m

Not started
🧪

Rob's Sections

Temporary: the three modules Rob is testing — Core Concepts, Discovery, and Search.

3 modules · ~1h 10m

Not started
🧪

Max's Sections

Temporary: the three modules Max is testing — Lineage, Metadata in Practice, and Data Quality.

3 modules · ~1h 20m

Not started
🧪

John's Sections

Temporary: the three modules John is testing — Ownership and Access, Capstone, and Data Products.

3 modules · ~1h 10m

Not started
🧪

Akshay's Sections

Temporary: the three modules Akshay is testing — Governance Automation, APIs, and Custom Integrations.

3 modules · ~1h 20m

Not started

All modules

What Is DataHub?

  1. What Is DataHub?
  2. What Is DataHub, Really?
  3. I'm sure you've run into this.
  4. What DataHub is NOT.
  5. DataHub is a Context Platform.
  6. One Catalog, Two Audiences
  7. DataHub builds context from what already exists -- plus what your team adds.
  8. Quick Exam

Core Concepts

  1. Core Concepts
  2. Datasets: Where Most Reasoning About Data Happens
  3. Datasets Contain Information About Your Data — Not Your Data
  4. Columns: What's Inside a Dataset
  5. Columns Don't Contain Your Data Either
  6. Tags: Quick, Informal Labels
  7. Glossary Terms: Your Official Business Vocabulary
  8. Tag or Glossary Term? Don't Mix Them Up
  9. Structured Properties: Your Organization's Custom Fields
  10. Domains: Where an Asset Lives in the Business
  11. Domains
  12. Datasets and Columns Are Entities — and They're Not Alone
  13. Every Entity Has Exactly One Unique Name: The URN
  14. DataHub Benefits for Data Users
  15. DataHub Benefits for Data Publishing and Governance
  16. How Metadata Gets Here: Ingestion and Enrichment
  17. How This Looks Day to Day
  18. Read a Dataset Page End to End
  19. Summary: The Everyday Concepts
  20. Quick Exam

Discovery

  1. Discovery
  2. What You Will Be Able to Do
  3. The Home Page
  4. The Search Bar: Your Primary Navigation Tool
  5. The Entity Page: Everything About One Asset
  6. Check the Trust Signals First
  7. Summary and Columns Tabs: What You See When You Open a Dataset
  8. Historical Schema Changes Are Not in the Main Column View
  9. Lineage Tab: Tracing the Data Journey
  10. Use Downstream Lineage as an Impact Analysis Tool
  11. The Remaining Tabs at a Glance
  12. Domains and Glossary: Browsing Without Searching
  13. Make DataHub Part of Your Daily Workflow
  14. Summary and What's Next
  15. Quick Exam

Search

  1. Search
  2. Search Objectives
  3. The Search Bar: What It Actually Searches
  4. Reading the Results Page
  5. Using Filters to Narrow Your Search
  6. When Fuzzy Isn't Enough: Exact Phrase Search
  7. Start Broad, Then Filter Down
  8. Don't Trust the First Result Blindly
  9. When You Don't Know What to Search: Browse by Domain
  10. Start with the Business Concept, Not the Table Name
  11. Find Everything Owned by Your Team
  12. Find the Dataset From a Business Question
  13. What are Views?
  14. How to Create a View
  15. Create a Public View for Your Team's Core Assets
  16. Natural Language Search (Supported Deployments)
  17. Find the Revenue Dataset You'd Trust
  18. Summary: Search and Discovery
  19. Quick Exam

Lineage

  1. Lineage
  2. What You'll Be Able to Do After This Module
  3. Lineage: A Map of How Data Flows
  4. Four Reasons Lineage Is Not Optional
  5. Finding the Lineage View in the UI
  6. Anatomy of the Lineage Graph
  7. Two Levels of Lineage Detail
  8. Understand the Transformation Chain Before You Use a Dataset
  9. Debugging a Broken Metric: Work Backwards Through Lineage
  10. A Lineage Gap Means "Not Tracked," Not "No Upstream"
  11. Use Downstream Lineage Before Deprecating or Changing a Table
  12. Trace a Broken Dashboard Metric
  13. Check for Understanding
  14. Summary: Lineage

Metadata in Practice

  1. Metadata in Practice
  2. Metadata in Practice
  3. What You See on an Entity Page
  4. Reading a Description as a Consumer
  5. Tags: Quick-Scan Classification Labels
  6. Tags vs. Glossary Terms vs. Structured Properties: When to Use Each
  7. Metadata Goes All the Way Down to Columns
  8. Don't Use Tags Where You Mean Glossary Terms (and Vice Versa)
  9. Deprecated: What It Means and What to Do
  10. How to Add Metadata (Hands-On Reference)
  11. Contribute to the Catalog Even If You're Not the Owner
  12. What are Structured Properties?
  13. Structured Properties in Practice
  14. Use Structured Properties for Governance Fields That Need Validation
  15. Documents -- Long-Form Knowledge on Any Entity
  16. Documents Are Supplemental, Not a Substitute for a Good Description
  17. Write Metadata for a Cold Dataset
  18. Check for Understanding

Data Quality

  1. Data Quality
  2. Data Quality in DataHub
  3. Assertions: Automated Tests for Your Data
  4. Freshness Assertions: "Is This Table Up to Date?"
  5. Volume Assertions: "Does This Table Have the Right Amount of Data?"
  6. Column and Custom SQL Assertions: "Is the Data Internally Consistent?"
  7. Navigating the Quality Tab
  8. Always Check the Quality Tab Before a Critical Report
  9. Passing Assertions Are a Floor, Not a Ceiling
  10. Configure a Freshness Assertion (DataHub Cloud)
  11. Freshness + Volume Together Cover More Ground Than Either Alone
  12. Which Dataset Would You Use?
  13. Incidents — When a Quality Failure Becomes an Operational Event
  14. Assertions vs. Incidents — Know the Difference
  15. What is a Data Contract?
  16. Contract Status — Passing vs. Failing
  17. Define Contracts for Datasets with Downstream SLA Commitments
  18. Data Health — The Catalog-Wide Quality View (DataHub Cloud)
  19. Before we move on.
  20. Recap + Looking Ahead

Ownership and Access

  1. Ownership and Access
  2. You Found a Dataset. Now What?
  3. Two Concepts, One Goal
  4. The Three Ownership Types
  5. One Asset, Three Owners
  6. How to Claim Ownership in DataHub
  7. Use Groups, Not Individuals
  8. Don't Claim Ownership You Don't Have
  9. Domain Assignment in Practice
  10. How to Assign a Domain in DataHub
  11. Access Policies -- Who Can Do What in DataHub
  12. Platform Policies vs. Metadata Policies
  13. Built-in Roles -- A Starting Point
  14. Scope Sensitive Privileges by Domain
  15. Orphaned Assets: The Hidden Cost
  16. Filter by Owner to Audit Your Portfolio
  17. Check for Understanding
  18. Summary + What's Next

Capstone: End-to-End Scenario

  1. Capstone: End-to-End Scenario
  2. The Revenue Discrepancy
  3. Capstone Tasks
  4. Capstone Debrief and Discussion

Data Products and Applications

  1. Data Products and Applications
  2. What is a Data Product?
  3. Data Products vs. Datasets
  4. Creating a Data Product
  5. Name Data Products After Business Capabilities, Not Tables
  6. What is an Application? (DataHub Cloud)
  7. Applications in Practice (DataHub Cloud)
  8. Data Product vs. Application -- The Relationship (DataHub Cloud)
  9. Create a Data Product for the Finance Domain
  10. Check for Understanding

Governance Automation

  1. Governance Automation
  2. What are Metadata Tests? (DataHub Cloud)
  3. Assertions vs. Metadata Tests — Know the Difference
  4. Example — High-Usage Assets Must Have Owners
  5. Start with Three Tests: Owner, Description, Domain Coverage
  6. What are Compliance Forms? (DataHub Cloud)
  7. When to Use a Compliance Form
  8. Use Compliance Forms for Time-Bound Governance Mandates
  9. Automations — Metadata That Moves Automatically
  10. Automation Requires Careful Scoping
  11. Configure a Metadata Test (DataHub Cloud)
  12. Check for Understanding

DataHub APIs

  1. DataHub APIs
  2. DataHub APIs: Automating Your Metadata Operations
  3. The UI Gets You Started. The API Gets Work Done.
  4. DataHub Exposes Two API Surfaces
  5. You Do Not Need to Read the Docs to Learn GraphQL
  6. The Core Read Operation: search
  7. Mutations: Writing Metadata Back to DataHub
  8. GraphQL Mutations Affect Real Data Immediately
  9. The REST API: Built for Bulk
  10. Match the API Surface to the Job
  11. The acryl-datahub Python SDK
  12. Write Queries and Delete Stale Entities
  13. Check for Understanding

Custom Integrations

  1. Custom Integrations
  2. When the Built-In Connectors Are Not Enough
  3. Four Reasons to Build a Custom Source
  4. The DataHub Python SDK: Two Ways to Emit
  5. MCPs: The Unit of Metadata Change
  6. URNs: How DataHub Identifies Every Entity
  7. Map Your Entities Before Writing Any Code
  8. Emitting Lineage: Connecting Your Assets to the Broader Graph
  9. MCEs Are the Old Format. Use MCPs.
  10. Validate Your Connection Before Emitting Anything
  11. Structured Ingestion Sources: When to Go Further
  12. Build a Custom Ingestion Script for InternalDB
  13. Check for Understanding
  14. Summary + What's Next
  15. Thank you!

Bonus: How DataHub Structures Metadata Internally

  1. Bonus: How DataHub Structures Metadata Internally
  2. The Building Blocks of DataHub
  3. What Is an Entity?
  4. Entities Have...
  5. Entity vs. Entity Type: Don't Mix These Up
  6. What Is an Aspect?
  7. Aspects Are Modular by Design
  8. What Is a URN? The Permanent Identity of Every Entity
  9. Always Reference Assets by URN in Scripts and API Calls
  10. Reading URNs Directly from the Browser URL
  11. What Is a Domain? The Organizational Layer
  12. How Entities, Aspects, URNs, and Domains Fit Together
  13. Explore a Live DataHub Instance
  14. Assign Domains Based on Who Governs the Asset, Not Who Uses It
  15. Check for Understanding
  16. Summary: The Four Building Blocks