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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 connecting raw data streams to various applications and analytics. Operational challenges complicate efforts to ingest raw data. Companies also often need to change the application architecture to shorten the data pipeline latency for time-sensitive apps. 

The next frontier in analytics

A new generation of streaming databases connects stream processing tools to database-like tools for building apps and managing data. These modern tools combine the low latency of stream processing tools with traditional database paradigms to store, process and retrieve data. Competitors include Confluent’s ksqlDB, NYC-based Materialize and several Apache Flink-based companies. 

Wu believes RisingWave is the only company to combine all the elements of modern data platform design from the ground up in the Rust programming language. Also, he decided to focus more on cost efficiency and ease of use rather than reducing latency. 

The platform uses a cloud-native, distributed architecture that separates compute and storage as part of the design. It also supports various deployment models across containers and service meshes. Enterprises can also ingest data from popular streaming services such as Apache Kafka, Redpanda, Apache Pulsar and AWS Kinesis. 

“We are making a bet that streaming is a new frontier for the data processing analytics field,” Wu said. “Streaming databases shorten the data pipeline cycle significantly. These systems provide the best opportunity to harness insights for event data with a short shelf life.”

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