San Francisco-based startup Splice Machine, which has developed a database management system specifically for hybrid clouds, has launched its own Online Predictive Processing Platform (OLPP) for powering the new generation of predictive applications that run both on premises and in clouds.
 
The 5-year-old company also revealed that it has raised an additional $9 million from existing investors Correlation Ventures, Interwest Partners and Mohr Davidow Ventures, in addition to first-time investor Salesforce Ventures.
 
Splice Machine has banked a total of $40 million in venture capital since its founding in 2012, according to Crunchbase.
 
The company claims that its software can make predictive analytics usable in real-time operational applications at big-data scale. By using the Splice Machine OLPP, applications can now both “predict” by learning from the past as well as use those predictions to “act in the moment,” the company said.
 
Prior to this OLPP platform, building a predictive application at big-data scale was either prohibitively complex or costly, the company said. Companies either had to hand-code components together—such as compute engines, fast key-value stores, analytical in-memory engines, streaming engines, machine learning libraries, and notebooks—or else use expensive scale-up packages, such as SAP HANA or Oracle Exadata, whose costs are unaffordable for many companies.
 
Splice Machine integrates the Apache HBase and Spark engines into one ANSI SQL Relational Database Management System (RDBMS) that enables a company’s existing staff–those already familiar with SQL–to build predictive applications. The OLPP has two deployment options: as database-as-a-service (DBaaS) and as an on-premise offering.
 
“Predictive analytics were a great starting point for the deployment of artificial intelligence, but they do not go far enough,” co-founder and CEO Monte Zeweben said. “The next generation of predictive applications make predictive analytics actionable in operational settings such as planning systems, maintenance systems and health care 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.
 
Read the full article on eWeek here.