Timecho Raises Over US$10 Million First Fund

Timecho, founded by the creators of Apache IoTDB, an IoT-native time series database, has announced a first round of funding of more than US$10 million led by Sequoia China and joined by Koalafund, Gobi China, and Cloudwise.

Time series database has thrived in recent years to meet new data management requirements for the Internet of Things (IoT) era. Developers are seeking a database that can optimize data ingestion rates, storage compression as well as query and analytics features that match the needs of their time-stamped data applications. Based on Apache IoTDB, a top-level open-source project ranked seventh by commits (individual changes to files) in 2021 among all Apache projects, Timecho is technically capable of delivering foundational infrastructure that powers demanding industrial use cases. Moreover, the Timecho team has over 40 patents and papers in industry-leading, time-series management journals including ICDE, SIGMOD, VLDB.

“Apart from its outstanding performance, Apache IoTDB is lightweight, easy-to-use, and deeply integrated with software in the big data ecosystem, including Apache PLC4X, Kafka, Spark, and Grafana,” said Dr. Xiangdong Huang, Founder of Timecho and PMC Chair of Apache IoTDB. “It has been widely used by multiple enterprises in the power industry, public transportation, intelligent factories and other scenarios. To expand its application in the IoT area and strengthen its performance to meet the specific requirements of enterprises, Timecho will continue investing aggressively in R&D to enhance features such as end-edge-cloud collaboration.”

“Timecho maintains intensive interaction with the open-source community,” said Dr. Julian Feinauer, Timecho Technical Director for the European Market and PMC member of Apache IoTDB. “With the funding, we will be able to accelerate R&D to help developers and companies best leverage the power of Apache IoTDB.”

Timecho will focus on providing solutions that will be more adaptable with high-end equipment, mass devices, computational-resource-limited platforms, and further scenarios.

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