Deploy the Open-Source Edition
qData Data Platform Open-Source Edition is the community edition. You can deploy qData Open-Source Edition using one of the following three methods.
| Deployment Method | Description | Applicable Scenarios |
|---|---|---|
| Docker Compose Deployment | Supports lightweight, full, and independent demo-data modes with one-command Docker Compose startup | Quick start for beginners, feature demos, test environments |
| Start Locally from Source Code | qData Data Platform source code runs locally by developers, while dependency components are started through Docker Compose | Daily development, feature joint debugging |
| Self-Managed Deployment (Pure Manual Installation) | All dependency components and qData Data Platform services must be manually installed and configured | Production environments, large-scale deployments, personalized customization scenarios |
1. Docker Compose Deployment
qData Data Platform and its dependency services are defined in Docker Compose configuration files. Choose a startup mode according to your verification needs:
light: Lightweight mode. Does not start DolphinScheduler or Spark and is suitable for quick verification.all: Full mode. Starts qData and all dependency services.demo: Independent demo-data mode. Start it separately when you need the built-in data source.
Characteristics:
- One-command startup with a low deployment threshold.
- Strong environment consistency, avoiding the problem of "it cannot run locally".
- Suitable for quick experience, feature demos, and test verification.
- Not suitable for large-scale production environments because scalability and operations capabilities are limited.
2. Start Locally from Source Code
The core qData Data Platform code is run locally by developers through IDE debugging or command-line startup. Dependency components such as databases, message queues, and computing engines are started through Docker Compose and provide service support.
Characteristics:
- Keeps the source-code runtime mode, making development and debugging easier.
- Containerizes the dependency environment and reduces manual configuration.
- Suitable for daily development and feature joint debugging by R&D teams.
- Faster than pure manual deployment, but has certain local resource requirements.
3. Self-Managed Deployment (Pure Manual Installation)
All components must be installed and configured by the user, including databases, message queues, schedulers, Spark, and other dependencies, as well as packaging and deployment of the data platform itself.
Characteristics:
- Flexible and controllable, allowing customization of component versions, parameters, and deployment architecture based on actual needs.
- Suitable for production environments or scenarios requiring deep customization.
- Requires strong operations and big data platform capabilities, with complex configuration and higher deployment cost.
- Closer to traditional enterprise operations practices.