San Francisco-based Striim has unveiled Striim Cloud, a real-time data streaming solution that offers data integration as a fully managed software-as-a-service platform. Stiim’s goal is to make it easier for companies to build data pipelines — an important consideration as companies move towards the use of more edge-sourced data.
Edge computing can help solve requirements for access to real-time streaming data needed for making critical decisions. However, integration of data streams from sensors, 5G-connected devices and edge AI platforms brings with it new challenges for handling more diverse datasets from more geographically dispersed sources. While not explicitly
“Handling and analyzing large-scale data for real-time decision-making and operations is an ongoing challenge for every enterprise; one that is only going to become more challenging as more data sources come online,” said Ali Kutay, founder and CEO of Striim, Inc. “These challenges are driving digital transformation. Striim Cloud is a powerful, cloud-based, SaaS platform that gives enterprises worldwide an invaluable advantage in reaching this goal.”
The shift towards digital transformation has demanded the use of more real-time data with quick response time. The company claims Striim Cloud delivers real-time autonomous data pipelines that allow businesses to evolve their existing architectures to cloud-native setups. The SaaS offering allows access to multiple applications, enabling its use for monitoring customer behavior, the performance of marketing campaigns, and also evaluating supply chain management.
Striim Cloud can be integrated with platforms such as Azure Synapse Analytics, Google Big Query, and Snowflake. Striim Cloud is also capable of merging live streams with external data for lambda views and analysis to perform queries and loads. For data recovery management, the software replicates data continuously in real-time to enable application availability.
Striim said it will be charging its customers based on a consumption basis, with pricing varying by the number of containers needed to run the service as well as bandwidth for data transfer.
AWS | Azure | data integration | data management | data pipelines | Google | Snowflake | streaming data | Striim