Sylentrix compute provides distributed computation and storage for consistently low-latency enrichment, querying, aggregation and stateful computation against event streams and traditional data sources.
Sylentrix compute offers unmatched performance, resilience and scale for real-time and AI-driven applications, at any scale.
Kubernetes cluster
Sylentrix compute can process data on a set of networked and clustered computers that pool together access memories (RAM) and cores to let applications share data with other applications running in the cluster.
When data is stored in RAM, applications run a lot faster since it does not need to be retrieved from disk and put into RAM prior to processing. Sylentrix stores and process data in RAM, spread and replicate it across a cluster of nodes; replication gives you resilience to failures of cluster members.
What you can do with Sylentrix Compute
The unique capability of Sylentrix Compute is its ability to process both batch and streaming data, with low latency and in real-time, enabling transactional and analytical processing.
You can request data, listen to events, submit data processing tasks using Sylentrix clients connected to a cluster.
You can run data pipelines for the data to flow from an application to a data source or from a data source to an an application or analytics database.
You can import data from databases, files, messaging systems, on-premise and cloud systems in various formats (data ingestion).
You can enriche, manipulate and run queries on the data.
You can store and monitor your data, and set triggers and notifications on anomilies.
You can run computational tasks on different cluster members (distributed computing).
You can store your data using distributed implementation of various data structures like maps, caches, queues, topics, concurrency utilities.
You can have multiple Sylentrix clusters at different locations in sync by replicating their state over WAN environments.
You can listen to the events happening in the cluster, on the data structures and clients so that you are notified when those events happen.
Example use cases
01.
Increase the transactions per second and system uptime in payment processing.
02.
Decreasing order processing times with low latencies
03.
Build a distributed topic (publish/subscribe server) to build scalable chat servers for customer services for online and smart[hones.
04.
Share datasets, e.g., table-like data structure, to be used by applications and hybrid commerce channels.
05.
Store session data in online commerce and web applications (enabling horizontal scalability of the web application).