Community responses to the question "What does the term “cloud-native geospatial” mean to you?"

cng-meaning-survey.md

• FAIR data principles and distributed computing
• data is stored in the cloud and can be directly read or queried with http read requests
• Exploiting large geospatial datasets in the cloud in an optimized way by transmitting as few bytes as possible.
• efficient with cloud storage
• Ability to scale up/out geospatial analyses to cloud scale more easily
• Big Data, tiled processing, STAC, portable/scalable workflows, COG
• Technologies that are designed to work well in the cloud.
• Less configuration
• The data (and analytics - which is not yet achieved) moves from Desktop computers to clouds (plural), where they can be accessed using cloud services by expert but also non-expert users.
• To work on cloud without lift and shift (I.e. spinning up a VM on cloud)
• No need to download data
• Processing/accessing geospatial data in a way that is optimised for cloud - scalable, parallelised, cost efficient
• Geospatial technologies, data, solutions, platforms, etc which are designed and optimised for the cloud.
• Ready to use streamable data access
• It means working "directly" on datasets in cloud storage organized for efficient use.
• Compute lives next to geospatial data on a public cloud. Data is in a format that supports users requesting only what they need, for both vectors and imagery. Data is not packaged up into arbitrary archival formats that must be interacted with as a whole.
• Conducting analysis in the cloud in data-adjacent compute, along with the tools and standards that make this possible
• Pretty much everything that comfortably runs in the cloud. aka not QGIS, ArcGIS Desktop / Arc/Map
• Geospatial solutions based on cloud infrastructures/oriented towards cloud processing
• A set of standards and tools designed to support easy and scalable geospatial data access access and manipulation through cloud technologies on the web.
• Works seamless with cloud services and infrastructure. Something that requires limited if any ETL working natively without customization.
• Allowing working with geospatial data without manually deploying servers, systems that are designed to be easily run in cloud services, Ideally super minimal infrastructure requirements for example just "S3 &lambda function"
• Data and services run in the cloud.
• Data storage, transfer all within the cloud. Processing/visualizing only the parts of the data that are relevant without the need to transfer the rest of the dataset. Processing data in parallel. Parallel reads AND writes
• Geospatial data designed for utility in and with agility of compute, storage, and network of the cloud (AWS, Google, etc.)
• Availability of large EO data sets and software in cloud services
• Primarily formats designed for read access "at rest" in cloud object stores (S3, etc), opening up access for random access use of very large data collections without the need to download the whole collection, or even a file.
• Data & tech stack deployed to a remote (on demand) server infrastructure
• Fast delivery of data from large cloud hosted datasets
• Data formats and tooling ecosystem that can be easily deployed to multi-cloud infrastructure, with common patterns/problems (eg: metadata catalogs, data ingestion and transformation, etc) abstracted into reusable patterns
• File formats that support partial reads
• Data that are designed to be accessed from a remote compute environment that is "close" to the storage location. No more downloading files to a local environment. Use only the parts of the data that you need.
• Data and processing tools designed to take advantage of cloud computing environment specially in terms of scalability and high throughput between processing and storage.
• Makes optimal use of cloud-based architectural patterns and components in ways that minimize wasted compute, unnecessary data transfer, and reduce task time-to-completion.
• Ability to use/store data in a cloud-based infrastructure
• data, software and services that perform seamlessly in various cloud environments
• Structured geospatial data allowing interoperability across provider by e.g., the use of standardised http range requests. But so much more!!
• Cloud-optimized geospatial data hosted in object storage with queryable metadata (STAC) that enables scalable cloud-native analysis and processing.
• Storage and analysis is preformed on, and optimized for, the cloud
• Direct to access geodata, no external download/storing the data. Also, I can run processing easily in the cloud without moving data around.
• Easier deployment and handling of geospatial data on the cloud
• Taking advantage of cloud technologies for storing and processing data.
• Cloud-native geospatial
• Fast analysis of large data with simple low overhead data management.
• Single data-store formats for optimized analysis/viewing, pyramids+blocks contigency for faster web viewing o consuming by data processing pipelines
• Without downloading data to local machine, and only fetching content that is needed, and ability to dk that on cloud as well.
• A cloud service that is provided focused on data analysis using geographical data
• Services provided in the cloud optimised for geospatial data analysis
• Whether on disk in local storage or in cloud, access the same way: define area and layer/channel/band/values of interest and say "please send me what's in this AOI".
• Big data sets too large to copy locally for analysis; best analyzed in-cloud (often parallelized)
• leveraging cloud ecosystem of services for geospatial workloads
• Hosted, accessible and consumable all in a cloud environment. Resulting in no “expensive” transport in time or expense and being useable away from a fat client on a desktop.
• GIS reliant on cloud for computation and storage