In the News

Companies are looking to get the most out of their myriad data streams, making applications and analytics a hot topic at all levels of an organization, across a variety of industries. As a part of the next generation of database solutions and a leader in Big Data innovation, Splice Machine is being talked about by technology and industry publications.

11.18.15  /  Splice Machine revs up RDBMS solution with Apache Spark

Splice Machine Inc. has added some analytical muscle to its SQL-RDBMS-on-Hadoop solution by tapping into Apache Spark’s in-memory technology. The result is a hybrid SQL database businesses can use to perform transactional and analytical workloads in tandem.

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11.18.15  /  New Splice Machine RDBMS unites OLTP and OLAP

Splice Machine yesterday announced the 2.0 version of its relational database management system (RDBMS), which aims to give you the scalability of Hadoop and the performance of Spark without the need to rewrite years' worth of SQL or retrain staff.

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11.18.15  /  Splice Machine 2.0 RDBMS adds Spark to speed SQL on Hadoop

Splice Machine announced Tuesday that the 2.0 version of its relational database management system – a hybrid in-memory RDBMS using the scalability of Hadoop coupled with the in-memory processing of Spark – is now available in public beta. The company is currently looking for testers.

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11.18.15  /  Splice Machine taps Apache Spark for in-memory data muscle

Today's vast data volumes have spawned a variety of new database contenders, each with particular strengths and features to recommend them. Splice Machine is one such upstart, and while it's always banked on the scale-out capabilities of Hadoop, on Tuesday it placed an accompanying bet on Apache Spark's

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11.17.15  /  Splice Machine Unveils 2.0 Version of RDBMS

Splice Machine today announced the 2.0 version of its RDBMS, the first hybrid in-memory RDBMS powered by Hadoop and Spark. Splice Machine’s version 2.0 delivers a database solution that incorporates the proven scalability of Hadoop, ANSI SQL, ACID transactions, and the in-memory performance of Spark.

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