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1. Get Started

Starter Journey is your roadmap for setting up Databricks the right way.

All about data

Get the data foundation right first. Every dashboard, ML model, and agent reads from the same tables. When those tables are wrong, everything downstream is wrong: reports mislead, models train on noise, agents hallucinate.

BI, ML, and GenAI have different requirements from that data, but all three share the same platform and the same catalog. Fix the foundation once and it serves all three.

This guide is the minimum steps, in order, from an empty account to a working production environment.

What you'll build

Each section starts where the last one left off, taking you from an empty Databricks account to a production-ready environment:

#SectionWhat you'll have when done
2Before you start
- Foundations
- Cloud tenant ready
A clear understanding of workspaces, Unity Catalog, and your cloud tenant model
3Infra setupWorkspaces provisioned, users and groups added, SSO activated
4Cost monitoringDay-zero visibility: imported usage dashboard, optional packaged dashboards, tags, and account budgets
5Governance strategyA catalog/schema structure that fits your organization's size
6Access your dataCloud storage connected, external systems accessible via managed connectors
7Build the first ETL pipelineA working medallion pipeline (bronze → silver → gold)
8Query and exploreInteractive SQL queries running against your lakehouse data
9Unified analyticsKPIs defined as Unity Catalog metric views; dashboards, Genie Spaces, and apps surfacing data to business users
10Predictive analyticsModels trained, saved to Unity Catalog, and scored in batch
11AgentsAI agents built on your data
12Orchestration using jobsThe pipeline running on a schedule with retries and notifications
13Data access controlRow, column, and object-level access controls on your catalog
14CI/CD and DevOpsProjects version-controlled and deployed with DABs and CI/CD

Work through the sidebar in order, or jump straight to the topic you need.