SPLICE MACHINE, INC.
SAN FRANCISCO, CA

About Splice Machine Splice Machine is disrupting the traditional database world with a hybrid data platform that unifies streaming, analytics and transactions in a single relational database system, removing latency, cost and complexity from supporting modern big data applications in industries such as Financial Services, Digital Marketing, Healthcare, E-Commerce and IoT. Companies leverage Splice Machine’s ANSI SQL support to accelerate offloading operational and analytical workloads from expensive Oracle, Teradata, and Netezza systems. We are headquartered in the South of Market (SOMA) neighborhood of San Francisco. Splice Machine replaces traditional RDBMS and Data Warehouse solutions, simplifying your architecture, reducing cost and improving scalability and performance. Splice Machine powers big data applications using industry standard SQL on a scale-out architecture so you can focus on the business logic. Data scientists continuously clean and transform raw data into features that provide machine learning models with true predictive signal. Using Splice Machine’s notebook environment and Spark integration, data scientists can easily leverage the speed of in-memory computation and transactional in-place data updates to rapidly experiment with new features, parameters, and models. This provides continuously improving predictive power with more accurate models that are trained more frequently in addition to real-time reports and dashboards. With Splice Machine, you can scale out dynamically when the need for capacity grows or back when it decreases, so you only pay for what you really need. Plus as a DBaaS, we have eliminated the complexity of the Hadoop stack. You provision, connect, and query. We make sure the containers are healthy, backed up and secure. Splice Machine replaces traditional RDBMS and Data Warehouse solutions, simplifying your architecture, reducing cost and improving scalability and performance. Splice Machine powers big data applications using industry standard SQL on a scale-out architecture so you can focus on the business logic. Data scientists continuously clean and transform raw data into features that provide machine learning models with true predictive signal. Using Splice Machine’s notebook environment and Spark integration, data scientists can easily leverage the speed of in-memory computation and transactional in-place data updates to rapidly experiment with new features, parameters, and models. This provides continuously improving predictive power with more accurate models that are trained more frequently in addition to real-time reports and dashboards. With Splice Machine, you can scale out dynamically when the need for capacity grows or back when it decreases, so you only pay for what you really need. Plus as a DBaaS, we have eliminated the complexity of the Hadoop stack. You provision, connect, and query. We make sure the containers are healthy, backed up and secure. What's Going on at Splice Machine How fast can Splice Machine ingest data? How much more storage does Phoenix require vs. Splice Machine? Please fill out the form below to receive more information on Splice Machine.