Streambased: Empowering Business Analysts with Real-Time Data Insights
As digital transformation accelerates across industries, companies are recognizing the untapped value in their real-time data streams. Enterprise streaming analytics firm Streambased aims to help organizations extract impactful business insights from these continuous flows of operational event data. In an interview at the recent event, Streambased founder and CEO Tom Scott discussed how their approach enables advanced analytics on streaming data.
The foundation of Streambased’s offering is an open-source event streaming platform, widely adopted by Fortune 500 companies, known as Apache Kafka. While Kafka reliably transports high-volume data streams between applications and microservices, conducting complex analytical workloads directly on streaming data has historically been challenging. To address this issue, Streambased adds a proprietary acceleration technology layer on top of Kafka, making the platform suitable for demanding analytics use cases.
Streambased leverages existing Kafka data pipelines to ensure that its analytical capabilities have access to up-to-date, clean, and well-organized data. This approach ensures optimal performance for use cases such as fraud detection in financial services, where analysts can quickly query related transactions to investigate anomalous activity. The convergence of operational and analytical data platforms represents the “streaming data lake” movement, bringing a complete convergence between data systems used for analytical purposes and those used for operational purposes.
Streambased remains focused on empowering business analysts through frictionless self-service access to granular real-time data, without requiring changes to existing tools and processes. With recent enhancements like infinite data retention in Kafka and native streaming analytics services, the foundation is set for this new paradigm of operational and analytical data platforms.
