The Intelligent SQL RDBMS for Application Modernization
Distributed ACID Transactions
Hybrid Transactional and Analytical Processing (HTAP)
Splice Machine isolates the resources allocated to OLTP and OLAP workloads from each other, so each can progress independently of each other. For analytical workloads, the Splice Machine provides the flexibility to allocate resources (CPU, memory etc.) at the user or group level. Resources can also be distributed across all the running queries or a minimum or maximum threshold can be specified. Combined with a multi-version concurrency control (MVCC) locking mechanism, this ensures that the performance of transactional workloads even when large reports or analytic processes are running.
Splice Machine allows you to seamlessly scale-out from gigabytes to petabytes as data volumes change. Splice Machine’s massively parallel scaling architecture can easily handle workloads that would overwhelm traditional databases. With Splice Machine Cloud Manager, configuring a new cluster is as easy as using a few sliders to set compute units for OLTP, OLAP, and ML processing, allocate storage, and schedule backup frequency and retention.
In-Database Machine Learning
Splice Machine’s ML Manager provides native data science functionality with in-database analytics, integrated Jupyter notebooks, and native workflow management and deployment capabilities with MLflow. It has never been easier to enable machine learning in a production application and provide the governance capabilities to audit models in production.
Splice Machine can be deployed on bare metal or Kubernetes whether your infrastructure is on-premises, AWS, Microsoft Azure, or Google Cloud (soon). Splice Machine on Kubernetes abstracts away the underlying infrastructure and makes hybrid cloud and multi-cloud deployments straightforward.