InfinyOn, a real-time event streaming company, announces the v0.10 beta release of the InfinyOn Cloud platform. Building an infrastructure for real-time event streaming is challenging, expensive to scale, and difficult to operate.
InfinyOn Cloud provides a modern approach that reduces the cost and complexity of operating and managing real-time data pipelines.
Enterprises can bring real-time intelligence to data pipelines for Edge and Cloud use cases with InfinyOn Cloud. “We were able to rapidly build an event driven architecture with end to end data pipelines that sourced MQTT data, with inline transformations and sink it to a postgres database” said Ben Cleary, CTO of Klarian. “InfinyOn Cloud has enabled Klarian to build highly tuned data models, delivering actionable insights within the energy sector.”
InfinyOn SmartModules, powered by WebAssembly, allows users to program unique functions, apply intelligence and enrich data. “WebAssembly allows small data processing modules to be moved anywhere at a fraction of the cost when compared with VMs or containers” said Alex Williams, Founder and Publisher for The New Stack. InfinyOn users can apply filters, mask private fields, remove invalid records, and use machine learning at the edge or in the Cloud in a single platform.
InfinyOn introduces a new concept of Stream, Transform and Load (STL), a fundamental paradigm shift from Extract, Transform and Load (ETL). Traditional ETL pipelines are batch driven and often consist of a fragmented architecture that is difficult to manage and includes lots of software or microservices to make data usable.
“Implementing the novel concept of STL, users can quickly build programmable data pipelines for real-time actionable insights.” said Grant Swanson, InfinyOn VP of Marketing. “Eliminating ETL tools creates substantial cost savings and a simplified architecture for our users.”
Industry First Data Transformation App Store – SmartModule Hub
SmartModule Hub is an industry-first app store where users can share and reuse data transformations to quickly build sophisticated data pipelines.
“SmartModule Hub is a centralized catalog that empowers engineers to build and share stream transformations across the organization.” said Infinyon CTO, Sehyo Chang. “Data Engineering, Data Science and Machine Learning Ops teams will benefit by leveraging existing SmarModules instead of writing custom transformations. The combination of STL and SmartModule Hub accelerates development and significantly reduces time to market.”
IoT Edge and Cloud Use Cases
Gartner predicts that “By 2025, more than 50% of enterprise-managed data will be created and processed outside the data center or cloud.” IoT big data statistics show that with the increased adoption of IoT devices, the amount of data generated will reach 73.1 zettabytes (ZB) by 2025. I&O leaders must plan for the growth of IoT and related data at the edge, protect their enterprise from edge growing pains in security and connectivity, and prepare for changes in edge computing use cases.
The InfinyOn Real-time Data Platform bridges the edge to the cloud. Distributing intelligence with SmartModules between edge and cloud plays a critical role in developing valuable insights for business decisions across all sectors and industries. InfinyOn Cloud can be used for numerous use-cases including predictive maintenance, supply chain automation and real-time inventory management while minimizing data transfers.
InfinyOn, a real-time event streaming company, has architected a data platform for event stream processing and real-time transformation.
Their flagship product, InfinyOn Cloud, is a fully managed Fluvio service, powered by Rust and WebAssembly, that allows organizations to deploy and operate large-scale data pipelines from edge to Cloud.
The data platform includes a SmartModule Hub that enables enterprises to program their data pipelines as they flow between producers and consumers for real-time services. Fluvio and InfinyOn Cloud were built using the Rust programming language known for its strong safety, small footprint, and low memory utilization.
