When it comes to scalability and redundancy, Big Data really proves its value. The solution has the ability to Shard (split) its data, which allows multiple servers to perform query operations simultaneously. Additionally, data is written on multiple instances simultaneously. It's core architecture includes the ability to absorb the failure of a master node and automatically elect the most up-to-date slave as the new master to keep the system functioning.
FireScope is horizontally scalable. Rather than buying bigger servers, we scale by adding additional servers. Built to handle large data sets, FireScope Stratis' use of multiple servers means you have all the resources you need to add compute, memory and storage capacity. As your data set gets bigger, there is no need to upgrade to expensive high-end hardware. This also means you can incrementally adopt newer and faster compute platforms without throwing out the models you had before. high transaction rate environments are easily supported because as more servers are added, transactions are distributed across the larger cluster of nodes, which linearly increases database capacity. With this model additional capacity can be added without reaching any limits.