Getting Started

Data Engineer

Goal: Ingest data and run through an ODS → DWD → ADS processing pipeline

Step 1 — Run through core features (30 minutes)

Lakehouse Quick Start Experience

Step 2 — Ingest your data

Step 3 — Build your data processing pipeline

Dynamic Table Incremental Computation · Studio Task Development and Scheduling · End-to-End CDC Complete Example

Step 4 — Connect external tools

JDBC Driver · cz-cli Command Line · SQLAlchemy · Python SDK

Data Analyst

Goal: Connect to data, run SQL, use AI-assisted analysis

Step 1 — Run your first SQL query (5 minutes)

Run Your First SQL Query

Step 2 — Connect your tools

Step 3 — Advanced analysis

Data Analytics Agent (DataGPT) · SQL Usage Guide · Experience Performance with TPC-H Sample Data

AI / ML Engineer

Goal: Build vector search, RAG knowledge bases, AI-enhanced analytics

Step 1 — Understand Lakehouse AI capabilities

Lakehouse AI Overview

Step 2 — Choose your scenario

Platform Administrator

Goal: Set up accounts, assign permissions, configure environments

1. Manage Users — Create users, assign roles
2. Create and Use Workspaces — Workspace isolation and configuration
3. Manage Workspace Users — Workspace-level permission management
4. Build a Data Development Environment Using Workspaces — Set up a complete data development environment for your team
5. Configure Monitoring and Alerting Rules — Alerts for task failures and performance anomalies

AI Agent / Automation

Goal: Call data capabilities through deterministic interfaces, build automated data pipelines