What is a DataOps Lab project?
Labs are innovation-driven example projects and design patterns built using the data product platform to achieve advanced functionality and uncover all our platform has to offer. These Lab projects explore advanced showcases around DataOps with Snowpark, Streamlit, Data Mesh, Data Products, etc. They show how to use DataOps.live with open-source tools and dbt packages to meet new and different needs.
DataOps Labs aren't meant for production, but in each Lab project, we'll guide you through building and deploying the pipelines till you see and approve the results. And while we will answer your questions on a best-efforts basis, the labs are not part of our official support. They are creative spaces to put new ideas into practice and build projects that answer ambitious needs around DataOps.
Why should you care?
Lab projects make it possible to discover new use cases you've been looking for or have thought not possible with DataOps.live, for example. They can help you accelerate the improvement and transformation of your data operations in a highly contextual manner and in conjunction with third-party tools to grow and meet DataOps lifecycle needs around Snowflake.
Start browsing available DataOps Lab projects
We'll periodically add new Lab projects, each offering a new concept or a tip to bring your projects to life. Every Lab has a README file that guides you through each process step. Check out the list of currently available Lab projects:
- Streamlit App Deployment to AWS Fargate : Deploy Streamlit apps developed on DataOps.live to AWS Fargate using AWS Cloud Development Kit (AWS CDK).
- Event Streams Pipeline Triggering: Run a pipeline based on an external action and parametrize it to change its runtime behavior.
- Data Product Dependencies: Handle complex data product dependencies with the data product engine and ensure that data products are built and managed more efficiently.
- Jira Integration Demo: See how to connect your DataOps project with your Jira boards to enable better tracking of issues.