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Traditional databases focus on data after it has been stored. Stream processing helps businesses take action on data as it’s being generated. These tools allow analytics and decision engines to respond to IoT events, user clickstreams and financial market data. But they also typically require specialized data engineering skill sets to deploy and scale.
RisingWave has raised $36 million to help simplify this process with a streaming database that combines elements of traditional databases and stream processing. RisingWave Cloud service is currently in private preview. The funding will help grow the business team for a broader launch next year.
Customers are already using the tools for various business-critical applications:
Real-time analytics and alerting analyzes millions of metrics to detect real-time anomalies.IoT device tracking creates a real-time dashboard that shows traffic using road sensors.Monitoring business trends by aggregating data about products and brands across social media.Pre-aggregating data from multiple sources to optimize online application data sharing. Streaming complexities
RisingWave CEO, Yingjun Wu, Ph.D., founded the company in early 2021 after a decade of working on stream processing tech at AWS and IBM. He told VentureBeat that existing database systems like AWS Redshift, Snowflake and BigQuery could not efficiently process streaming data. At the same time, existing streaming processing tools were too complicated to use and operate at scale.
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“Building real-time applications leveraging streaming data should not incur operational overhead and become a barrier to entry,” he explained.
Popular stream processing tools like Apache Flink and Samza require multiple big data services and use Java-based APIs that can be difficult to learn. In addition, these systems combine compute and storage together, which complicates scalability.
Developers face numerous challenges connecti …