Getting Started
Choose an onboarding path based on your role. Most starter scenarios can be completed within 30 minutes.
Data Engineer
Goal: Ingest data and complete an ODS → DWD → ADS processing pipeline
Step 1 — Try the core features (30 minutes)
Lakehouse Quick Start Experience
Step 2 — Ingest your data
| Data Source | Recommended Method |
|---|---|
| MySQL / PG / Oracle, real-time CDC | Studio Real-time Sync Tasks |
| Full-database migration, multiple tables at once | Multi-table Real-time Sync |
| Object storage (S3 / OSS / COS) | Pipe Continuous Ingestion · COPY INTO |
| Kafka message streams | Kafka Pipe |
| Local CSV / Excel files | Upload Local Data |
Step 3 — Build data processing pipelines
Dynamic Table Incremental Computation · Studio Task Development and Scheduling · Data Engineering Agent (ETL development and task management in natural language) · End-to-End CDC Example
Step 4 — Connect external tools
Data Analyst
Goal: Connect to data, run SQL, use AI-assisted analysis
Step 1 — Run your first SQL (5 minutes)
How to Run Your First SQL Query
Step 2 — Connect your tools
| Tool Type | Connection Method |
|---|---|
| FineBI / Power BI / Tableau and other BI tools | JDBC Driver |
| DataGrip / DBeaver / Navicat and other clients | MySQL Protocol |
| Python scripts | SQLAlchemy |
| Terminal command line | Command-Line Client |
Step 3 — Advanced analysis
Data Analytics Agent · SQL Usage Guide · TPC-H Sample Data Performance Walkthrough
AI / ML Engineer
Goal: Build vector search, RAG knowledge bases, AI-enhanced analytics
Step 1 — Learn about Lakehouse AI capabilities
Step 2 — Choose your scenario
| Scenario | Entry Point |
|---|---|
| Semantic search / RAG knowledge base | AI Data Preparation · Vector Search |
| Call LLMs from SQL | AI Functions (AI_COMPLETE / AI_EMBEDDING) |
| Manage and switch between multiple LLM models | AI Gateway |
| Conversational data analysis in natural language | Data Analytics Agent |
| ETL development, task management, and operations diagnostics in natural language | Data Engineering Agent |
| Python data processing + AI inference | ZettaPark Quick Start |
Platform Administrator
Goal: Set up accounts, grant permissions, and configure environments
- Quickly Add and Manage Users — Create users and assign roles
- Quickly Create and Use Workspaces — Workspace isolation and configuration
- Quickly Manage Workspace Users — Workspace-level permission management
- Build a Data Development Environment with Workspaces — Set up a complete data development environment for your team
- Quickly Configure and Use Monitoring and Alerting Rules — Task failure and performance anomaly alerts
AI Agent / Automation
Goal: Use deterministic interfaces to call data capabilities and build automated data pipelines
| Scenario | Recommended Integration |
|---|---|
| SQL execution and result retrieval | cz-cli sql · Python connector |
| Task scheduling and triggering | cz-cli task / runs refill |
| Studio task development and data source management | cz-cli task create/save · Studio Task Development · Studio Data Integration |
| Scenario | Recommended Integration |
|---|---|
| Python data read/write | ZettaPark · clickzetta-connector |
| Business semantic layer queries | Semantic Views |
| Collaborate with specialized data sub-agents | cz-cli agent run |
| Browser automation Web Agent | Singclaw |
Quick Start by Feature
| What I Want to Do | Entry Point |
|---|---|
| Quickly experience core product features | Lakehouse Quick Start Experience |
| Learn the Studio interface layout | Lakehouse Studio Quick Tour |
| Upload a local CSV file | Quickly Upload and Import Local Data |
| Real-time CDC sync from MySQL / PG | Studio Real-time Sync Tasks |
| Create a scheduled sync task | Quickly Create Sync Tasks to Import Data |
| Mount S3 / OSS / COS object storage | External Volume |
| Configure ETL scheduling workflows | Quickly Configure and Schedule ETL Workflows |
| Run federated queries on a data lake (Hive / Iceberg) | External Catalog Federated Query |
| Configure data quality rules | Quickly Configure and Use Data Quality Rules |
| Configure monitoring and alerting | Quickly Configure and Use Monitoring and Alerting Rules |
| Experience engine performance (TPC-H) | Experience Engine Performance with TPC-H Sample Data |
| Write complex business analysis SQL | SQL Usage Guide |
| Use AI to analyze data conversationally | Data Analytics Agent |
| Use AI to develop ETL / manage tasks | Data Engineering Agent |
| Build vector search / RAG knowledge base | Vector Search |
| Process data with Python (ZettaPark) | ZettaPark Quick Start |
| Migrate from Spark to Lakehouse | Migration Guide |