Decodable Inc., a startup working to ease the task of processing data in real time, today announced that it has raised a $20 million early-stage funding round from a group of prominent investors. 

Venrock and Bain Capital Ventures led the Series A round. Former U.S. Chief Data Officer DJ Patil participated as well, along with DataDog Inc. Chief Executive Officer Olivier Pomel, Cockroach Labs Inc. CEO Spencer Kimball and Redis Ltd. Chief Revenue Officer Jason Forget. 

Processing business data in real time, that is immediately after it’s generated, can provide major benefits for companies. If a manufacturer has the ability to process equipment error logs in real time, then it can detect malfunctions immediately after they emerge and quickly carry out repairs. Retailers, banks and other organizations can also benefit from the technology in various ways.

The challenge Decodable has set out to address is that many companies struggle to implement real-time data processing workflows. The main reason is that the software tools commonly used to process data in real time are highly complicated, requiring specialized skills to use. 

Decodable has built a cloud-based service that promises to simplify the task. The service makes it possible to create real-time data pipelines in a few minutes, the startup says, and spares developers the hassle of managing the underlying infrastructure. Data pipelines can be created using the SQL query language, which many developers are already familiar with. 

A data pipeline built with Decodable’s service can be used to move information between different systems. A delivery company, for example, could stream operational information from its logistics applications to a centralized data lake in real-time. Decodable says that data pipelines created using its service can also perform computations on the information they transport. This makes it possible to perform tasks such as filtering unnecessary data points. 

According to Decodable, its service includes a mechanism that ensures records transported through a data pipeline are processed only once. The feature helps companies avoid scenarios where multiple copies of the same record are sent to a system, which can lead to errors. 

Decodable built its service to support a wide range of enterprise use cases. Artificial intelligence developers can use the service to process the datasets with which they train their neural networks. According to Decodable, its technology also makes it easier to implement the data mesh architecture, an emerging approach to analytics that is becoming increasingly popular in the enterprise. 

Supporting software container workloads is yet another use case that Decodable targets. A containerized application is usually not a single program but rather multiple software modules, or microservices, that regularly exchange data with another. Decodable eases the task of transporting data among an application’s microservices. 

Decodable is led by founder and CEO Eric Sammer, an early Cloudera Inc. employee who later became a vice president and distinguished engineer at Splunk Inc., the publicly traded data analytics company. Sammer led development of Splunk’s real-time data processing technology and cloud infrastructure before founding Decodable.

“We built Decodable to let every developer and data engineer easily access real-time data in minutes, and we’ve removed the infrastructure needs so companies no longer have to be dependent on costly and large data platform teams,” Sammer said. 

Prior to the Series A funding, the startup raised a $5.5 million seed round led by Bain Capital Ventures. 

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