DataPipeline Raises Tens of Millions of RMB in Series A+ Round, Serving Starbucks and Longfor | FreeS Fund Investment News
Better understand data, apply data, and respect data with reverence.
" Investor Perspective
Li Feng, Founding Partner, FreeS Fund
Email: feng@freesvc.com
Over the past two years, DataPipeline has made significant strides in productization and customer expansion, securing partnerships with several globally recognized brands and enterprises. This demonstrates that the team's technical experience and product capabilities from Silicon Valley have successfully combined with the Chinese market and delivered tangible results. It also confirms that demand for data application, management, and structuring is clearly growing among Chinese enterprises.
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DataPipeline Closes Tens of Millions of RMB in Series A+ Funding
Source: DataPipeline
DataPipeline, a company focused on providing one-stop real-time data and application integration platforms and solutions, recently announced the completion of a Series A+ round worth tens of millions of RMB. The round was led by Baidu Venture (BV), with Matrix Partners China and Qingliu Capital participating.
Shortly after its founding, DataPipeline secured several million RMB in angel funding from FreeS Fund. In 2018, it raised 21 million RMB in Series A funding led by Matrix Partners China, with FreeS Fund and other institutions participating.
Over the past year, DataPipeline has successfully served large enterprise clients including Starbucks, Goldwind Science & Technology, Longfor, Caitong Securities, and Dingdang Medicine, covering retail, manufacturing, finance, energy, and internet sectors. The company has also established strategic partnerships with dozens of upstream and downstream industry partners.
▲ DataPipeline Founder Chen Cheng
"If we think of data as a product, DataPipeline wants to build an intelligent unmanned store where customers can freely choose what they need based on their own requirements," said Chen Cheng, founder of DataPipeline. "There's a saying in this industry: 'Garbage in, garbage out.' If the data being transmitted is garbage, the value produced from it will also be garbage. DataPipeline has always been committed to ensuring data quality and traceability."
As the big data era unfolds, business departments are developing greater awareness of and demand for data, while technology departments hope to better empower business operations through intelligent, platform-based approaches. However, excessive data volume, rapid change, and numerous data sources have left enterprises with data that is often unknown, inaccessible, uncontrollable, and unusable.
To address this, DataPipeline has adhered to DataOps principles since its founding in 2016 — focused on providing one-stop data and application integration platforms and solutions:
- Driving data sharing — Data producers, consumers, and managers can clearly understand what data exists within the enterprise, and data users can browse, search for, and retrieve it.
As a real-time data fusion product, DataPipeline requires no code and enables real-time data pipeline connections through a visual, graphical configuration interface in minimal time. Both management and ordinary employees can use DataPipeline to locate relevant database information without relying on DBA engineers.
- Driving real-time data flow — Data circulates among business departments and data applications, generating new results through computation and continuously producing new value. As enterprises adopt more business systems, data volume grows faster. Yet when business departments need data that has already been collected, they must request it from IT departments, resulting in long response cycles.
DataPipeline supports automated data exchange across multiple heterogeneous data sources and destinations, currently supporting more than 20 mainstream options. By providing batch-and-stream-integrated data integration services, it enables continuous processing of infinite datasets on an uninterrupted timeline. It not only captures real-time changes at the source but also consumes them promptly, ensuring real-time data transmission.
- Driving data management — Data governance and management have long been heavily promoted but poorly executed within enterprises. For small-scale data teams, the task is particularly daunting.
DataPipeline provides users with batch-and-stream-integrated data integration, data quality management, API data access, metadata management, task scheduling, and visual operations and monitoring services, helping enterprises build a solid foundation for efficient and accurate business analysis.
The company stated that this round of funding will be used to further accelerate the development of its data fusion platform's core capabilities, continuously optimize industry customer service, strengthen product, R&D, and marketing teams, and refine its customer acquisition and customer success systems.
Going forward, DataPipeline is committed to helping more users better understand data, apply data, and respect data.
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