Ellipsis Drive

🗺️ 𝗥𝗮𝘀𝘁𝗲𝗿 𝗱𝗮𝘁𝗮 𝗶𝘀 𝗮 𝗴𝗼𝗼𝗱 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 𝗼𝗳 𝘄𝗵𝘆 𝗼𝗻𝗲 𝘀𝘁𝗼𝗿𝗮𝗴𝗲 𝗮𝗯𝘀𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗻𝗲𝗰𝗲𝘀𝘀𝗮𝗿𝗶𝗹𝘆 𝗳𝗶𝘁 𝗮𝗹𝗹 𝗱𝗮𝘁𝗮. Its spatial relationships are fundamental to how it needs to be stored, accessed and processed, something a tabular abstraction doesn't naturally preserve. The result? More complexity gets pushed downstream onto the data team.

Ellipsis Drive catalogs spatial data across existing storage, helping teams and applications find and use it without moving source files.

Putting everything into one system sounds simpler. But does every type of data actually benefit from being managed in the same way? 🤔 Satellite imagery, sensor streams, and analytical datasets all come with different access patterns and performance requirements. Trying to optimize one infrastructure for every workload can quickly create trade-offs. The more useful question may be how to make different types of data work effectively together across the organization. 🌍 #Geospatial #DataInfrastructure #SpatialData

Ellipsis Map Engine extends Ellipsis Drive to run Python and AI workflows in parallel across large datasets, with less manual preprocessing.

While Agentic AI can reason, it still needs an external compute resource to run its logic. Such logic may include geospatial computations. 🧠 The future of spatial AI depends on connecting the intelligent reasoning from LLMs with a solution that can run any geospatial logic against any geospatial data in a scalable way. #AgenticAI #GeospatialData #SpatialComputing

Storage choices affect how data is used. Explore three questions about workloads, application access, and changing requirements. Read the Blog -> Start With the Data, Not the Storage