Next-Gen DMAC

Next-Generation Data Management and Cyberinfrastructure

A collaboration between RPS Group Ocean Science and the NOAA Integrated Ocean Observing System (IOOS)

This public repository will describe the prototyping efforts and direction of the Next-Gen DMAC project, "Reaching for the Cloud: Architecting a Cloud-Native Service-Based Ecosystem for DMAC." The goal of this project is to identify the technological and process shifts needed to develop a cloud-native architecture that will serve the current and future needs of the IOOS community. We will be testing a variety of technologies to identify more efficient cloud processing, storage, and data collection options while experimenting with cloud-native architectural patterns to bring it all together.

For a great resource explaining the background information for the Next-Gen DMAC project, check out This Slideshow.

We hosted a DMAC Tech Webinar in June 2023 explaining some of the results of the project so far.

This repository is intended to be a collaborative working area for open discussion about cloud-based services for ocean science. Please feel welcome to start a Discussion, contribute your ideas, and even contribute to our prototyping efforts!

If you are new to contributing to GitHub, check out these links:

• Creating a Pull Request
• Getting started with GitHub (video)

View the full documentation at our GitHub Pages site.

Related Projects

We are addressing a wide variety of topics and collaborating with others to solve related problems.

See a list of those projects here.

Steering Committee Meeting Minutes

The Steering Committee meets every quarter to discuss project updates and vet ideas for future development.

The meeting minutes are here.

Technical Documentation

Completed prototypes have been documented alongside their code.

• Kerchunk Ingest for NOS model data
• Argo Workflows Test of QARTOD and IOOS Compliance Checker
• Jupyter notebooks:

Prototype Plan

The planned prototypes seek to demonstrate a modern, interconnected system. Visit the links below to learn more about each prototype design and considerations.

Overall System Architecture

• Kubernetes
• Nebari (Data Science Platform)

Data Ingest

1. Workflow Management
2. Event Messaging

Data Storage and Discovery

3. Scientific data store
4. Metadata Catalog
5. Catalog Queries

Data Processing

6. Real-Time Analytics
7. Dask Processing

Data Analysis and Presentation

8. Jupyter Notebooks
9. Restful Grids: App Data Access
10. Client-side Rendering

Prototype Relationships

The prototype system we are initially exploring is a combination of several open-source components that we are configuring to run in AWS. This diagram illustrates the planned relationships between the components as well as the expected interactions by various user groups.