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One storage and computing engine can support mixed workloads such as OLTP, OLAP, and time series, enabling a single database to serve multiple data applications.
Innovation on the capability of incremental materialized view make the real-time visibility of data updates happen, including data updates and deletions synchronized to downstream data tables.
The flexible and unified architecture support the seamless migration for deployment, applications and data on and off the public cloud, self-built data center and edge node.
With cloud-native fine-grained management technologies such as storage and computing separation, read and write separation, and serverlessization, MatrixOne helps to reduce storage costs, achieve the ultimate elastic expansion and contraction.
MatrixOne's efficient vectorized execution engine ensures extremely fast analysis performance. Also, consistency protocols support updates, deletions, and real-time point queries of data.
Flexible computing node and data node architecture support on-demand resource adjustment for different loads, and efficiently respond to load changes and differentiated performance expectations.
MatrixOne consolidates streaming data ingestion, real-time analytics, and data transformation in a single system to eliminate database sprawl and reduce maintenance costs for IoT platforms.