Data-driven space exploration

Organizations pursuing missions in space need quick access to their data so they can support mission-critical systems, ongoing software development and analytics applications.

Last year, NASA pushed into new territory, sending a probe to the outer corona of the sun for the first time. In March, SpaceX launched its commercial human transport vehicle, the Crew Dragon, which successfully docked with the International Space Station. This summer marks the 50th anniversary of the first Apollo moon landing, an event witnessed by an estimated 600 million people around the world on their black-and-white television sets. By 2024, we could see the Artemis moon landing, where the next man and the first woman land at the south pole of the moon.

What do these past, present and future missions have in common? Data. The data NASA captures is analyzed for years to come -- allowing the pace of space exploration to quicken. And data ultimately ensures the safety and security of space vehicles and their passengers. NASA’s Twins Study, for example, has provided insights into how human health is sustained while in space. Data from past missions will enable safer journeys in the future -- particularly as crews prepare for the ExoMars mission in 2020 where a rover and a surface platform will land on Mars to search for signs of life and conduct in-depth analysis of the planet.

The amount of data gathered through space exploration is, one could say, astronomical. While this data leads to further innovation and advances in space exploration, it has other tangible applications. It can be used for making real-time weather predictions with greater precision, understanding climate change and working to pre-empt natural disasters -- saving lives and preventing economic damages. It can also support larger scale AI initiatives such as communication during missions.

The strides being made by leveraging data are just the beginning of what can be accomplished, and the pace of these advances and innovations relies on the ability to process the data. A data-centric storage architecture is essential. It has five key attributes:

  • It delivers shared data, fast. Applications today can’t exist in the world of “slow” -- i.e., disk storage. Modern architectures should be built on flash and designed from Day One to be shared because tomorrow’s applications expect shared data.
  • It supports on demand consumption and automated delivery to accelerate innovation and reduce costs.
  • It is exceptionally reliable and secure to protect sensitive data.
  • It supports hybrid storage by design. Storage volumes should easily move to and from the cloud, facilitating application and data migration and enabling hybrid use cases for app development, deployment and protection.
  • It is constantly evolving and improving. Users expect the cloud to deliver more value every year for the same or lower cost, without downtime. Storage architecture must also be architected for continual improvement.

To continue down the path of rapid scientific advancement and to fully realize the benefits data has to offer, it must be shared, automated, secure and evolving. Data that is locked away cannot be leveraged; the right data strategy must be in place from the beginning.

A data-centric architecture should be built to provide shared data services for a wide range of data customers in the organization: developers delivering mission-critical production and web-scale applications, teams implementing a modern continuous integration/continuous delivery pipeline and the data science teams driving analytics and AI initiatives. Organizations pursuing missions in space need quick access to their data so they can support mission-critical systems,  ongoing software development and analytics applications.

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