Real-Time Stream Processing

Scalable and robust engine for processing and acting on streaming data

Data-driven applications need to process data as it arrives, acting on data in real-time to be able to deliver information to applications and users as quickly as possible. However, existing technologies, born in a batch processing world, were not designed for this.

Streamlio address this need with proven, best-of-breed technology for real-time data processing. Powered by the Heron real-time engine developed and proven in production at Twitter, Streamlio enables enterprises to deliver data-driven applications that can meet demanding SLAs at any scale.

Learn about Heron's architecture from co-creator Karthik Ramasamy


Real-time stream processing

Streamlio’s event-based design makes it possible to process data immediately, as it arrives. Heron’s granular controls allow you to tune latency and throughput to meet demanding SLAs with end-to-end latencies in tens of milliseconds

Proven solution

Streamlio builds on Heron, the streaming data processing engine hardened and proven in production at Twitter. As a replacement for Apache Storm, Heron has been handling hundreds of critical real-time applications that processes trillions of events daily

Scalable performance

With a multi-tenant architecture that ensures workload isolation, the ability to tune throughput and latency for individual workloads, and native execution of real-time processing jobs, Heron can meet demanding SLAs even as data and workloads scale


Automated monitoring and healing features regulate data flow and processing to automatically address bottlenecks and ensure resiliency even as workload demands fluctuate and failures occur

Development flexibility

Support for multiple processing semantics–at least once, at most once, effectively once–together with support for native execution of workloads written in languages including Java, Python, and C/C++ meet diverse development needs

Microservices design

Modular, extensible architecture designed to take advantage of containers and frameworks such as Docker, Kubernetes and Mesos to optimize resource management and provide deployment flexibility

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Report: Benchmarking Streaming Platforms

Industry analyst firm Gigaom surveys the technology landscape for streaming messaging and compares Apache Kafka and Apache Pulsar performance

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Webcast: Connecting Microservices

Streamlio's Karthik Ramasamy explains how you can connect microservices using Streamlio