Close-to-the-metal architecture handles millions of OPS with predictable single-digit millisecond latencies.
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Close-to-the-metal architecture handles millions of OPS with predictable single-digit millisecond latencies.
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Read MoreIn today's fast-paced digital landscape, processing real-time data is no longer a luxury but a necessity. Companies are increasingly relying on stream processing systems to meet the demand for immediate insights and actions. In this talk, we explore how you can achieve cost-efficient stream processing using RisingWave and ScyllaDB. RisingWave, an open-source distributed streaming database, offers seamless integration with ScyllaDB to create a powerful and scalable stream processing platform.
I will delve deep into the technical details, elaborating how RisingWave's decoupled compute-storage architecture handles streaming data at ease. After that, I will showcase how users can seamlessly route processed results to ScyllaDB for storage and low-latency serving. This dynamic process ensures that you always have access to the latest, most up-to-date results so as to make wise decisions in real time.
Yingjun Wu is the founder of RisingWave Labs (https://www.risingwave.com/), a database company developing RisingWave, a distributed SQL database for stream processing. Before running the company, Yingjun was a software engineer at the Redshift team, Amazon Web Services, and a researcher at the Database group, IBM Almaden Research Center. Yingjun received his PhD degree from National University of Singapore, and was a visiting PhD at Carnegie Mellon University. He has been working in the field of stream processing and database systems for over a decade.
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