MTS now supports raster weather data
Process raster weather data into tilesets to be rendered on dynamic and engaging weather maps. Read more about the private beta in our latest blog post.
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Cost effective scaling
We save developers money on data processing. MTS integrates custom datasets of any scale into maps faster, cheaper, and with more flexibility and control. This allows developers to focus on app development—not mapping infrastructure.
Parallelized data processing
MTS processes massive sets of data into custom vector tilesets, continuously updating the maps as the data changes. MTS uses a distributed and parallelized processing architecture that is 90% faster than traditional infrastructure.
Designing big data in Studio
Data uploaded using MTS can then be styled in Studio and the maps can be composited with any other vector tilesets. Designers control every aspect of the tileset—from the performance to the visual design—using recipes.
We designed the service to process big data that is constantly changing, and transform it into maps served out to our more than 650 million monthly active users -- supporting the scale of huge customers like Facebook, Snap, the Weather Channel, Tableau, and Shopify. MTS gives developers enterprise-grade data processing scale and reliability without the need to spend time and money building, deploying, and maintaining infrastructure.
Custom maps with big data made easy for developers
MTS provides APIs for developers to create custom maps. It is designed to process massive and complex sets of data into custom vector tilesets and continuously updates maps as the data changes. Instead of making multiple API calls and querying a slow backend database, a map that uses preloaded custom layers connected to vector tilesets is smooth and wicked fast, especially for big datasets with millions of points, lines or polygons.
Recipes
Recipes are configuration rules that tell MTS how to process data into vector tiles. Recipes provide fine control over vector tile generation, including simplification, zoom level extent, geometry unioning, attribute manipulation, and more. See all the available configuration options in the Recipe reference.
Live Weather Data
Yahoo! Japan uses MTS to create time series visualizations of live weather in Japan that update every 5 minutes. MTS saves their engineers time and money while providing a better experience for their users, letting Yahoo! Japan avoid upkeep on legacy mapping infrastructure and focus resources on improving the core app experience.
Designing in Studio
MTS doesn't just keep your data optimized and fresh -- it also keeps your data beautiful. Tilesets generated using MTS can be styled in Mapbox Studio and composited with any other vector tilesets, leveraging the full visual power and flexibility of the Mapbox platform.
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Private Data
Maps are locked and set to private by default, giving developers total control over who is looking at the data. Mapbox never accesses customer created data layers. Our management interface empowers developers to create, revoke, and monitor access control tokens directly. For more information, see our Tokens API documentation. SAML single sign-on is also available for all Mapbox customers, enabling teams with multiple users to collaborate securely.
Regionally redundant and auto-scaling caching network
MTS runs on Mapbox’s global platform, allowing data to be immediately available across multiple regions. This means it is automatically backed by our regionally redundant, auto-scaling caching network, making it seamless to scale, in any geography, with low latency and high throughput. Multiple regions with dynamic routing makes Mapbox’s infrastructure resilient to single-datacenter or single-region events like natural disasters and internet infrastructure outages.
Live Air Quality Alerts
The scope of poor air quality caused by fires on the West Coast is incredibly apparent in the data collected Plume Labs. Tilesets generated using MTS can be used in any map styles and can be composited with any other vector tilesets, read the full documentation.
