Minimal response-time latency and high reliability data processing.
Decreases data convergent complexity and reduces both operating and capital expenditure.
Rapid time-to-market with low Code / No code system to launch new services. Git integration for version control.
Streamline, integrate and process high-volume of data from multiple data source points.
Highly flexible to integrate legacy infrastructure to advanced streaming infrastructures.
End-to-end data visualization providing transparency in data processing to minimize errors.
Online and offline large scale data streaming, collection, transformation, and distribution such as Kafka (Avro), MQSeries, RabbitMQ, MQTT for IoT.
Connects and queries data from several different data sources at the same time and providing the same data to several systems simultaneously.
3G / Diameter / 5G / IoT / Fixed networks. Integrates with all third-party applications through open APIs such as HTTP(S), RESTful, SOAP, TCP/IP, UDP.
High availability, scalable and container-enabled for cloud deployments.
Support structured, unstructured, semi-structured and raw data types. Agnostic format support.
Wide range of support for numerous data stores such as Oracle, MS SQL Server, MySQL, Postgres, Aerospike, Casandra, HBase, Hadoop/Parquet, Kudu, MongoDB , HDFS.