Splice Machine said Wednesday that it has closed an additional $3 million in its Series B funding. The investors were Correlation Ventures, Roger J. Sippl and Roger Bamford. Splice is a developer of a SQL-compliant database for big data applications.
SAN FRANCISCO, Aug. 6, 2014 /PRNewswire/ — Splice Machine, provider of the only Hadoop RDBMS, today announced a $3 million extension to its Series B financing. Venture capital firm Correlation Ventures, along with well-known database industry luminaries Roger J. Sippl and Roger Bamford, provided the additional investment to help Splice Machine accelerate its product development and build out the sales and marketing teams for its Hadoop RDBMS. The funding signals the investors’ strong support for an RDBMS with affordable scale out that can replace traditional databases, and expands the initial $15 million Series B investment from Interwest Partners and Mohr Davidow Ventures (MDV), bringing the total round to $18 million.
This extension adds database icons to Splice Machine’s investor team. Roger J. Sippl was the Founder, Chairman and CEO of Informix Corp., a leading database provider until it was acquired by IBM in 2001. Joining Oracle in 1984, Roger Bamford was one of the founding members of the Company’s database team and is known as the founding father of Oracle Real Application Clusters and Oracle’s grid products.
“I’ve been in the enterprise software technology business for over 30 years, including being a pioneer of the database market,” said Sippl, a successful serial entrepreneur and Partner at Sippl Investments, LLC. “I invested in Splice Machine because it provides a Hadoop-based, scale-out SQL alternative to legacy databases such as Oracle and MySQL. Splice Machine is a compelling solution for those whose databases become too costly or difficult to scale when facing the explosive data growth occurring at most companies.”
Splice Machine received its first round of funding from MDV in October 2012. Since then, the Company has closed a $15 million Series B round, tripled its staff, engaged with 15 charter customers, and announced its public beta launch that includes a free download of the database. Splice Machine’s charter customers come from a variety of industries, including digital marketing, telecom, and high-tech, that are looking to take advantage of its scale-out SQL technology.
“When faced with the choice between a costly scale up of a legacy database or moving to a key-value store that requires impractical rewrites for existing applications, databases users are looking for a better, and more cost-efficient way forward in the age of Big Data,” said Bamford, until recently the Principal Architect of Server Technologies at Oracle. “Splice Machine caught my attention for its ability to enable app developers to enjoy the best of both worlds: RDBMS features like transactions, the familiarity and breadth of the SQL ecosystem, but with the limitless scalability on commodity hardware so touted by the NoSQL community.”
“This is such an exciting time for our Company, and best of all, this is just the beginning of our growth,” said Monte Zweben, co-founder and CEO, Splice Machine. “We’re delighted to have the attention and backing from the team at Correlation Ventures and respected database experts like Sippl and Bamford. We look forward to providing affordable scale-out for companies looking to power real-time operational applications and analytics.”
For more information about Splice Machine, please visit www.splicemachine.com.
About Splice Machine
Splice Machine’s Hadoop RDBMS is designed to scale real-time applications using commodity hardware without application rewrites. The Splice Machine database is a modern, scale-out alternative to traditional RDBMSs, such as Oracle®, MySQL™, IBM DB2® and Microsoft SQL Server®, that can deliver over a 10x improvement in price/performance. As a full-featured SQL-on-Hadoop RDBMS with ACID transactions, the Splice Machine database helps customers power real-time applications and operational analytics, especially as they approach Big Data scale.