30 Best Small Companies To Watch 2018

Messaging, processing and stream storage in a unified solution: Streamlio


The Streamlio team came together because of a shared belief in the opportunity and future of real-time data. They knew from first-hand experience that companies today are forced to cobble together a patchwork of incomplete technologies because an accessible solution for enterprise-grade real-time data processing did not exist–nothing available was production ready, seamless, durable, end-to-end, and proven at scale for real-time requirements. As a result, the team found themselves spending the vast majority of their time trying to solve these problems rather than creating actual solutions.

Therefore, they founded Streamlio to deliver the first unified real-time solution for the data-driven applications of the future. They share a passion for creating an open solution with a vibrant community and ecosystem and also care deeply about collaborating with their customers and partners, sharing their expertise and helping others to succeed.

The Vision of Success

The future of data is real-time. To be data-driven, enterprises will need to understand and respond to data as it arrives, not limit themselves to analytics that operate on historical, after-the-fact data. This shift has profound implications for how organizations receive, transform, and react to data. Streamlio believes that this future is only possible with a unified solution that makes real-time streaming applications easy to build, adapt, and scale reliably.

Power Data-Driven Applications

Today’s data-driven architectures require processing streaming data as it arrives, in real-time. However, building data-driven applications means painstakingly stitching together multiple disconnected technologies that were never designed for the scale and capabilities of enterprises, resulting in complex and fragile applications that are prohibitively challenging to build, operate, and scale.

Streamlio saw that data-driven applications demand a unified solution. It delivers that solution, bringing together proven open-source technologies for messaging, processing, and storage of streaming data in real-time.

  • Build faster - Streamlio's unified solution provides the components needed to power data-driven applications, eliminating incompatibility and complexity for developers.
  • Deploy with confidence - Built on technology proven in production at companies including Twitter and Yahoo!, processing billions of events and messages per day with full resiliency and no data loss.
  • Future-proof your applications - Uniquely architected for growth and scale so that you can easily keep up with the ever-growing demands of data, applications, and users.


Streamlio provides a modular solution powered by best-of-breed open-source technologies proven in production at companies including Twitter and Yahoo! Its team comprises key architects and PMC members responsible for these technologies.

Q. Stream Storage - Scalable distributed storage solution for streaming data

Streaming data messaging and processing require a robust storage solution to ensure scalability, performance, and data durability. Storage that is just patched into messaging or processing technology forces compromises that impact performance, durability, and scalability.

Streamlio uses Apache BookKeeper as its foundational storage system for streaming data. Apache BookKeeper is a scalable, fault-tolerant, and low-latency storage service optimized for streaming data. Originally developed at Yahoo!, BookKeeper has been widely adopted by enterprises including Twitter, Yahoo! and Salesforce to store and serve mission-critical data in a variety of use cases.

Q. 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 addresses this need with proven, best-of-breed technology for real-time and streaming data processing. Building on the streaming processing capabilities in Apache Pulsar and the Streamlio team’s experience as co-creators of the Apache Heron* real-time engine at Twitter, Streamlio enables enterprises to deliver data-driven applications that can meet demanding SLAs at any scale.

Q. Streaming Data Messaging and Queuing - Unified messaging and queuing designed for performance, durability and ease of use

Rapidly moving data between sources and applications is critical to handling streaming data. However, legacy technologies for messaging and streaming are cumbersome to use and burdensome to operate.

Streamlio provides best-in-class messaging and queuing powered by Apache Pulsar*, the open source streaming messaging solution developed and hardened in production at Yahoo! to support demanding applications including Yahoo! Mail, Yahoo! Finance, Yahoo! Sports, Flickr, and the Gemini ads platform. Pulsar has been proven in production, delivering robust messaging with the simplicity, durability, and performance needed for data-driven applications that can scale to handle over 1 million topics and over 100 billion messages per day.

Meet the leader

CEO and Co-Founder, Lewis Kaneshiro

Lewis brings extensive experience in data science and applied analytics to Streamlio. Prior to Streamlio, Lewis was a lead Data Scientist at Shazam, where he developed algorithms that used predictive analytics to create Shazam features. Previously, Lewis used analytics in financial services at Goldman Sachs, Falcon Investment Group, and Hall Capital Partners. Lewis holds a Masters degree in Statistics from Stanford University and Bachelor’s degrees from MIT in Mathematics and Management Science.

“With a multi-tenant architecture that ensures workload isolation, the ability to tune throughput and latency for individual workloads, and stream-native processing jobs, the Streamlio platform can meet demanding SLAs even as data and workloads scale.”