Splice Machine today announced the launch of its Online Predictive Processing Platform (OLPP) for powering the new generation of predictive applications. Splice Machine is the scale-out OLPP platform that can simply and seamlessly make predictive analytics actionable in real-time operational applications at big data scale. Using the Splice Machine OLPP platform, applications can now both “predict” by learning from the past as well as use those predictions to “act in the moment.”

Prior to Splice Machine’s OLPP platform, building a predictive application at Big Data scale was either prohibitively complex or costly. Companies either had to duct-tape compute engines together, such as fast key-value stores, analytical in-memory engines, streaming engines, machine learning libraries, and notebooks, or use expensive scale-up solutions, like SAP HANA or Oracle Exadata, whose costs are untenable for most companies.

Splice Machine’s OLPP platform uniquely integrates the Apache HBase and Apache Spark engines into one ANSI SQL Relational Database Management System (RDBMS) that enables a company’s existing staff – already familiar with SQL – to build predictive applications. The Splice Machine OLPP Platform has two deployment options – as an affordable, easy-to-use database-as-a-service (DBaaS) and as an on-premise offering.

Splice Machine also announced that it has raised an additional $9 million from existing investors Correlation Ventures, Interwest Partners and Mohr Davidow Ventures, as well as first-time investor Salesforce Ventures.

“Enterprises no longer need to suffer from the debilitating complexity of building predictive applications or the latency caused by the separation of operational and analytical systems,” said Bill Ericson, general partner, Mohr Davidow Ventures. “We see a whole new class of OLPP applications emerging in industries such as retail, manufacturing, logistics, financial services and healthcare.”

“Predictive analytics were a great starting point for the deployment of Artificial Intelligence, but they do not go far enough,” said Monte Zweben, co-founder and CEO, Splice Machine. “The next generation of predictive applications make predictive analytics actionable in operational settings such as planning systems, maintenance systems, and healthcare systems. We’re removing the complexity for companies that need to predict, plan and act in real time in order to keep up with customer demand.”

Early use cases such as supply chain optimization, predictive maintenance, predictive marketing, fraud detection and healthcare are generating significant benefits. ClearSense, a smart data solution for healthcare organizations, is currently using Splice Machine to support its predictive application for healthcare settings. By gaining faster access to, and more value from, the data in their systems, ClearSense is able to both predict and mitigate dangerous code-blue events, such as sepsis shock, which can be a matter of life and death.

“By combining prediction with action, we can proactively recommend to clinicians to check on their patients before it’s too late and they get into trouble,” said Charles Boicey, chief innovation officer, ClearSense.

To learn more about the Splice Machine, the OLPP platform for predictive applications, visit www.splicemachine.com.