Splice Machine has released version 2.0 of its hybrid in-memory RDBMS powered by Hadoop and Spark. Splice Machine 2.0, in public beta, integrates Apache Spark, a fast, open source engine for large-scale data processing, into its existing Hadoop-based architecture.
Splice Machine 2.0 offers a database solution that incorporates the scalability of Hadoop, ANSI SQL, ACID transactions, and the in-memory performance of Spark for high performance for mixed OLTP and OLAP workloads. It is ideal for a variety of use cases, including: digital marketing, ETL acceleration, operational data lakes, data warehouse offloads, IoT applications, web, mobile, and social applications, and operational applications.
The Splice Machine RDBMS offers a hybrid of in-memory technology from Spark and disk-based technology from Hadoop. Unlike in-memory only databases, the Splice Machine RDBMS does not force companies to put all of their data in-memory, which can become expensive as data volume grows. It uses in-memory computation to materialize the intermediate results of long-running queries but uses the power of HBase to store and access data at scale.
Splice Machine 2.0 includes advanced resource management to ensure high-performance for simultaneous OLTP and OLAP queries. With separate processes and resource management for Hadoop and Spark, the Splice Machine RDBMS can ensure that large, complex OLAP queries do not overwhelm time-sensitive OLTP queries. Users can set custom priority levels for OLAP queries to ensure that important reports are not blocked behind a massive batch process that consumes all cluster resources.
With the latest version, the Splice Machine RDBMS also adds an extensive management console. Staff can use it to monitor queries in process and visualize each step in the execution pipeline. This includes monitoring of batch import processes, with the ability to see import errors in real-time.
The new architecture includes the ability to easily access external data and libraries. The Splice Machine RDBMS can execute federated queries on data in external databases and files using Virtual Table Interfaces (VTIs). It can also execute all pre-built Spark libraries (over 130 and growing) for machine learning, stream analysis, data integration and graph modeling.
Splice Machine is currently looking for testers for the public beta of its 2.0 RDBMS. To become a tester, or to gain more information, please visit the link below.