1. About this guide
This guide helps you work with the Data area of Groundzero: bring data in, find it, query it, trace where it came from and agree what it means. Each chapter starts with what you can get done, then walks through it step by step with screenshots, then lists every field and message you may meet.
The names and values in the screenshots are examples from a sample company, "Acme Retail".
Start with what you want to do#
| I want to... | Read |
|---|---|
| Connect a database, bucket, SaaS app, AI provider or lakehouse | Data source connections |
| Upload or download files, or keep dashboards in a folder | Files and workspaces |
| Find a table or column, add tags or a description | Data catalog |
| Create a warehouse, run SQL, export a result, protect a table, back up a database | Lakehouse and warehouses |
| See what feeds a table, or what breaks if it changes | Data lineage |
| Agree and publish what business terms mean for AI Chat and agents | Ontology Studio |
For SQL itself, see the Lakehouse runtime guide.
Where to find things in Groundzero#
The top navigation bar has six items. Four belong to this guide.
| Navigation item | Chapter |
|---|---|
| Workspaces | Files and workspaces |
| Connections | Data source connections |
| Lakehouse | Lakehouse and warehouses |
| Catalog | Data catalog, Data lineage and Ontology Studio (use the menu on the left of the Catalog page) |
| Designer | Build documentation: datasets, visualizations and dashboards |
| DE/ML | Build documentation: notebooks, ETL jobs, runtimes and MLOps |
The Catalog menu also has Consumption Metrics, which is in the Analyze and Admin documentation.
How the pieces fit together#
- A connection tells Groundzero how to reach a source or a target.
- Datasets and ETL jobs read and write through connections. Tables written to a lakehouse warehouse can be queried with SQL.
- Each time a job or dataset runs, Groundzero records lineage: which tables it read and wrote, and which columns map to which.
- The catalog lists everything and lets you tag and describe it.
- Ontology Studio uses lineage to propose business definitions that you review and publish for AI Chat and agents.
How to read the chapters#
Most chapters follow the same pattern: the task and its outcome, numbered steps, a reference of fields and options, what happens next, tips, and troubleshooting as symptom, cause and fix. Messages in quotation marks are the exact text you see on screen.
| Convention | Meaning |
|---|---|
| Bold | A label you see on screen (button, tab, field) |
Code |
Something you type, or a permission name |
| Tip box | A shortcut or good practice |
| Note box | Something worth knowing |
| Warning box | A limit, a surprise or a risk |
Notifications
Success and error messages appear in the bottom-right corner. They stay for 10 seconds. Identical messages are shown once, and all are cleared when you go to another page.
Index of features#
| Feature | Chapter | Where to find it |
|---|---|---|
| Create, test, edit, delete a connection | Data source connections | Connections |
| Connector reference | Data source connections | Connector reference |
| Dashboards in workspaces | Files and workspaces | Workspaces |
| Browse, upload, download, delete files | Files and workspaces | Workspaces > My Files |
| Find resources by name, column or tag | Data catalog | Catalog |
| Descriptions (with AI drafts) and tags | Data catalog | Catalog > Overview |
| Warehouse, table and column tags | Data catalog | Catalog > Schema |
| Call a dataset or job from outside | Data catalog | Catalog > Contract |
| Create and delete warehouses | Lakehouse and warehouses | Lakehouse |
| Sessions, schema tree, running queries | Lakehouse and warehouses | Lakehouse > a warehouse |
| Page, download and export results | Lakehouse and warehouses | Query editor |
| Saved queries | Lakehouse and warehouses | Lakehouse > Saved Queries |
| Table properties and maintenance | Lakehouse and warehouses | Tree > Properties |
| Data policies and Table Access Manager | Lakehouse and warehouses | Tree > table menu |
| Audit log | Lakehouse and warehouses | Tree > audit > logs |
| Backups, schedules, restore | Lakehouse and warehouses | Tree, Schedules, Backups |
| Open a table's map, depth, columns | Data lineage | Catalog > Data Lineage |
| Impact of a change | Data lineage | Details panel > Impact |
| Runs, owners, insights | Data lineage | Details panel |
| Complete movement, export | Data lineage | Map, Export |
| Create a model, review definitions | Ontology Studio | Catalog > Ontology Studio |
| Draft with AI, fix missing sources | Ontology Studio | Toolbar, Review |
| Concepts and join suggestions | Ontology Studio | Review |
| Publish, releases, drift, export | Ontology Studio | Status bar, Releases |
Related guides#
- Lakehouse runtime guide: SQL, tables, maintenance and examples.
- Build documentation: notebooks, ETL jobs, datasets, runtimes and MLOps.
- Analyze, AI and Admin documentation: visualizations, dashboards, chat and agents, settings, billing, Consumption Metrics and permission administration.