Splice Machine Announces Limited Product Release
October 23, 2013
Company invites evaluators to validate authentic use cases, test SQL coverage and benchmark performance; initial results show a 10x improvement in hardware price/performance over Oracle databases
San Francisco, CA – October 23, 2013 – Splice Machine, provider of the only real-time transactional SQL-on-Hadoop database designed for Big Data applications, today announced that it has launched its Limited Release program. As part of the program, the Company is seeking 50 evaluators to validate authentic use cases, test SQL coverage and benchmark performance, prior to releasing the product for general availability.
“As the only real-time, transactional SQL database built on Hadoop, our product allows application developers and database architects to have the scalability of Hadoop and HBase, the ubiquity of SQL as well as the transactional integrity of an RDBMS,” said Monte Zweben, CEO and Co-Founder of Splice Machine. “Our current charter customers are seeing compelling results from our product and we’re excited to extend our Limited Release program to 50 additional evaluators.”
Splice Machine is currently being tested by its charter customers as a replacement for their Oracle, MySQL, or PostgreSQL databases. It is being used as the platform for real-time applications for customers in the consumer marketing, eCommerce, SaaS, and energy trading industries. Initial results have shown a 10x increase in price/performance, through a 30-50% decrease in response times on significantly less expensive hardware.
“This year, SQL has been the major battleground where vendors are planting their stakes with Hadoop,” said Tony Baer, Principal Analyst at Ovum. “Splice Machine differentiates itself from the pack by focusing, not on analytics, but bringing transaction processing to Hadoop. Capitalizing on research published by Google with the Percolator project, Splice Machine is adding a transaction layer to HBase that brings to OLTP the linear scalability of Hadoop.”
Splice Machine is looking to work with organizations that have one or more of the following situations:
- An existing Oracle, IBM DB2, MySQL or PostgreSQL database that is not scaling or is too expensive
- A Hadoop deployment that needs real-time SQL to unlock the data for data scientists
- Are considering NoSQL solutions for scale out but would benefit from leveraging their existing SQL people, tools and applications
Companies must have a specific use case to address, preferably one that requires real-time updates, such as real-time applications or operational analytics. They must also use SQL trained resources. Experience participating in other early access programs, including open source products, is preferred, but is not required. Additionally, experience with Hadoop is preferred, but not required.
For those companies looking to add Splice Machine to an existing Hadoop infrastructure, Splice Machine works with all major distributions, including the MapR Distribution for Apache Hadoop.
“It’s no longer enough to just have SQL on Hadoop. Solutions also need to be real-time and transactional to power the next generation of Big Data applications,” said Bill Bonin, Vice President of Business Development of MapR Technologies. “Companies should consider Splice Machine on MapR if they’re looking for a real-time SQL-on-Hadoop solution.”
Splice Machine expects to have a general availability release this winter, pending evaluator feedback. Splice Machine will be available on the website via a freemium model for development and limited node usage, with broader usage available with license fees at a fraction of the cost of traditional scalable RDBMS solutions.
About Splice Machine
Splice Machine is the only transactional SQL-on-Hadoop database for real-time Big Data applications. Splice Machine provides all the benefits of NoSQL databases, such as auto-sharding, scalability, fault tolerance and high availability, while retaining SQL – the industry standard. It optimizes complex queries to power real-time OLTP and OLAP apps at scale without rewriting existing SQL-based apps and BI tool integrations.