Splice Machine Cloudifies Its Database Management System for AWS

DBMS enables a new generation of smart, predictive applications designed to integrate fast data streaming, transactional workloads, analytics and machine learning.

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San Francisco-based startup Splice Machine, which makes a database management system fine-tuned for hybrid clouds, has made public a release of its database-as-a-service platform on Amazon Web Services Marketplace.

The company made the announcement at Strata + Hadoop World New York 2017 on Sept. 25.

Using this DBMS, the company claims that enterprises can develop and deploy a new generation of smart, predictive applications designed to integrate fast data streaming, transactional workloads, analytics and machine learning to improve continuous predictions and decision-making.

Splice Machine said it designed the platform to be easy to configure, provision and deploy, while being fast and scalable. It is aimed specifically for highly interactive modern applications that can learn from previous engagements, predict how to react for future interactions and then act in the moment as needed.

A key advantage of Splice Machine DBaaS is its support for industry-standard Structured Query Language (SQL), which allows customers to move existing database applications to the service while using existing skill sets.

With the Splice Machine DBaaS platform, users get:

  • Elastic scalability:  From gigabytes to petabytes, this service is designed to deploy faster, perform better, and scale as you grow, while you only pay for the capacity your applications need at any given time
  • Easy to operate: Configure and manage the powerful scale-out architecture by simply specifying the amount of compute power and storage required for Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) workloads on the Cloud Manager– Splice Machine does the rest
  • Full ANSI-SQL: Industry-standard SQL, Atomicity, Consistency, Isolation, Durability (ACID) compliant transactions, secondary indexes, referential integrity, triggers, stored procedures and more – capabilities that most applications require and that are already familiar to developers and database administrators (DBAs)
  • Hybrid Transactional and Analytical Processing (HTAP): Powerful HTAP database that can run Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) workloads concurrently, faster and at a fraction the cost of traditional relational DBMS (RDBMS) systems
  • Hybrid storage: Use disk-based or in-memory processing, as well as row-based or columnar storage, for each task to achieve proven performance across all workloads
  • AI and data science services: Ingest fast data, streaming from IoT devices and other data sources; use machine learning to develop, test and deploy models, integrating directly with application data; collaborate using notebooks and analyze data using industry-leading visualization tools
  • What-if planning on time-series data: Easily develop planning systems that can manage time-changing data such as inventory or currency
  • World-class operations: Manage scale elastically, optimize price performance trade-offs, manage availability and backup/restore options, create virtual network isolations, and monitor status and performance in real time across the whole stack.

“The days of static applications are over,” said Monte Zweben, co-founder and CEO of Splice Machine.

For more information, go here.

Chris Preimesberger

Chris Preimesberger

Chris Preimesberger is Editor of Features & Analysis at eWEEK, responsible in large part for the publication's coverage areas. In his 12 years and more than 3,900 stories at eWEEK, he has...