Global Serverless Data Cacheing
Build a complete globally-distributed cache in minutes that serves data to your apps and APIs in milliseconds
Problem: Cloud Databases are incredibly flexible and easy to use. Problem is that they get expensive as you have more data and users start to hit the database platform. Features like autoscaling, can increase the monthly cost of your database exponentially.
Solution: The GDN acts a local cache to cloud databases and transparently caches, serves, stores and updates data 25X faster than cloud databases. The GDN can offload expensive cloud data operations by serving them 20X faster and 90% cheaper than using cloud databases like AWS DynamoDB or Google Firebase only.
Example: Click here to see a sample application in Javascript with live source code that compares the performance of a standard cloud database versus the same database fronted with the GDN
Problem: IoT and applications that process events send a lot of data to the cloud. The cost and time of transit, storage, and processing in the cloud can quickly add up. Moreover, once data has been sent to the cloud, all the noise and redundant bits are discarded making transit an incredibly wasteful operation.
Solution: Macrometa lets customers ingest, process and augment or transform data at the edge. Macrometa's pipelines let you remove noise in data such as duplicates and enrich data by performing anomaly detection, feature recognition and pattern recognition in real-time close to the source of data. This eliminates the transit, processing and storage cost of raw data by up to 90%.
Example: Click here to see a real time high frequency trading bot for crypto currencies. This example trades against live crypto data. You can also get the source code here.
Problem: Online games require low latency to provide a great user experience.
Solution: Macrometa provides real-time pub / sub and data analytics to help game developers power games with incredible user experience. Use Macrometa's pub/sub as a way to share game state, player movement and synchronization between clients while collecting data for analytics transparently in real time.
Example: Click here to play a simple multiplayer game that uses Macrometa's stream and pub/sub to propogate player movement updates. Get the source here.
Problem: While architectures like Single Page Apps (SPAs), JAMstack allow you to serve the static part of your app from the CDN edge instantly, once the app is up and running on the client, it still needs to make dozens of REST and GraphQL calls to a centralized database far away to fetch and render data. Writing APIs is hard work. What if there was a way to build APIs quickly and serve them from the edge to SPAs and JAMStack apps?
Solution: Macrometa turns queries into functions as a service or Lambdas instantly. Now you can build CRUD APIs by just writing database queries and Macrometa will automatically turn them into executable Lambdas that are deployed globally. The lambdas now serve hot, queryable and mutable data from the edge 10ms away from your end-users and devices. Your JAMStack or SPA simply calls the http end point presented by the lambda and gets its data instantly from the closest Macrometa edge server.
Example: Click here to see a sample application with full source code on using Macrometa with JAMStack
Problem: Collecting millions of data points from devices deployed widely creates massive data accuracy and value challenges.
Solution: Macrometa provides simple and easy APIs to stream IoT data to edge locations for processing before sending to the cloud for longer term analysis and retention
Example: The animated clip shows a real world fleet tracking application using Macrometa as an edge event processor. Contact us to learn more and get access to the source code.
