From the very first days, we're developing Cube Cloud as the easiest way to build, test, deploy, and manage Cube projects. It provides fully-managed infrastructure, a rich set of workspace tools, a free tier for proof-of-concept projects, and a solutions engineering team to help you focus on building your semantic layer.
Today, we're happy to introduce demo deployments in Cube Cloud. They provide a convenient way to explore how Cube Cloud works by using a pre-configured data model and a demo dataset. If you'd like to learn data modeling by example or you're not yet ready to connect your production dataset, demo deployments come in handy.
Under the hood
Demo deployments are powered by DuckDB, which Cube proudly integrates with, and backed with CSV files in a public S3 bucket that serve as a demo dataset.
Each deployment comes with a sample data model that demonstrates basic concepts of data modeling in Cube (e.g., cubes, dimensions, measures, joins) and digs deeper into advanced topics like views, subquery dimensions, calculated measures, non-additive measure decomposition, dynamic data modeling with Jinja and Python, access control, programmatic configuration, and many more.
Creating a demo deployment
To take a look at a demo deployment, create a Cube Cloud account and proceed to create a new deployment. At the data source connection step you'll be prompted to enter credentials for your own data source or to create a demo deployment.
Watch the following video to for a step-by-step walkthrough:
Demo deployments are available to all Cube Cloud users.
Please check them out and share your feedback in our Slack community of more than 9,000 data practitioners. Any suggestions on how we can better educate Cube users are very much welcome.