The Splice Machine RDBMS, powered by Hadoop and Spark

The Splice Machine RDBMS combines the best of a traditional RDBMS with a hybrid in-memory and scale-out infrastructure. Enterprises will find all of the key functionality they have in their current RDBMS databases in the Splice Machine RDBMS:

  • Joins
  • Secondary indexes
  • Aggregations
  • Reliable updates through ACID transactions
  • Ability to support a high concurrency of small reads and writes

With in-memory technology from Spark, the Splice Machine RDBMS will outperform traditional RDBMSs for mixed OLTP and OLAP workloads.

Learn more about the features of the Splice Machine RDBMS.

Why Use It?

The Splice Machine RDBMS was designed to replace overwhelmed RDBMSs like Oracle, MySQL, IBM DB2 and Microsoft SQL Server that companies are finding are too expensive to scale. Splice Machine provides cost-effective scale out on commodity hardware, but unlike NoSQL databases, it provides standard SQL, eliminating the need to rewrite existing applications.

What is it?

The Splice Machine RDBMS is a unique hybrid database that combines the advantages of SQL, the scale-out of NoSQL and the performance of in-memory technology.

The Splice Machine RDBMS offers real time updates with transactional integrity, distributed, parallelized query execution and high concurrency on a flexible general-purpose database platform.

Splice Machine is built on three proven technology stacks:  Apache Derby, HBase/Hadoop, and Spark.  This allows for distributed, parallelized query execution that works with all of the standard Hadoop Distributions.

Splice Machine is a general purpose RDBMS that can be used for a variety of applications, but common ones that can benefit from the scale of Hadoop include Digital Marketing, ETL Acceleration, Operational Data Lake, Operational Applications, Operational Analytics and Internet of Things.

Learn more about the features of the Splice Machine RDBMS.

More information

Forbes Magazine talks about the Splice Machine Hadoop RDBMS


Splice Machine.stack.spark