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SAN FRANCISCO, Nov. 20, 2018 /PRNewswire-PRWeb/ -- Splice Machine, provider of a leading data platform to power intelligent applications, today announced that Andy Pavlo, Assistant Professor in the Computer Science Department at Carnegie Mellon University, has been added to the Company's technical board of advisors. Pavlo will bring technical expertise in database management systems, specifically distributed and autonomous architectures for large-scale transaction processing and analytical systems.
Dr. Pavlo is a member of the Database Group at CMU, a leading research collective that focuses on database systems, data mining and machine learning, and the Parallel Data Lab, academia's premiere storage systems research center at Carnegie Mellon University. He is the recipient of a 2018 Sloan Research Fellowship, the 2018 SIGMOD Best Paper award, and several faculty research awards from Google and Facebook. Pavlo has been invited to give talks all over the world, most recently at the Percona Live – Open Source Database Conference, SIGMOD, DataEngConf in NYC, In-Memory Computing Summit, and various universities.
"I'm excited to work with the Splice Machine team to solve some of the biggest database challenges for real-time, data intensive applications," said Pavlo. "Splice Machine is working in an interesting space right now. Splice Machine's data platform provides a scale-out architecture for hybrid transactional and analytical (HTAP) workloads that are important for enterprises looking to leverage machine learning technologies on their operational data sets."
Andy is the quintessential technical advisor. He and his students are breaking new ground in research published at the recent SIGMOD and VLDB conferences but he also has extensive industrial database experience as an early member of the H-Store/VoltDB research team led by Michael Stonebraker.
Splice Machine is a scale-out SQL RDBMS, data warehouse and machine learning platform in one. Since launching in 2012, the Company has helped companies migrate underperforming SQL workloads to the modern scale-out architecture, helped operationalize data lakes (often labeled as data swamps) by making them real-time, and helping enterprises build greenfield AI applications in a fraction of the time, money, and staff it takes to do so in a bespoke manner.
Splice Machine works with companies to build AI applications for use in financial services, healthcare, manufacturing, retail, and more. These intelligent apps can generate lasting business and customer benefits by reducing costs, boosting efficiency, and finding answers to customer's most challenging problems in real time. For example, in healthcare Splice Machine helps predict patient conditions and proactively advises doctors and nurses to save lives. In manufacturing, Splice Machine helps companies predict late orders and demand spikes to optimize supply chains and reduce inventories. Splice Machine also helps companies spot fraud earlier to proactively safeguard business and consumers.
To learn more about Splice Machine and its offerings, please visit http://www.splicemachine.com.
About Splice Machine Splice Machine is a new AI data platform for digital transformation. Unlike other Big Data platforms that provide offline, batch analysis, Splice Machine powers intelligent applications that are woven into the operational workflows of companies. It is a scale-out SQL RDBMS, data warehouse and machine learning platform in one. Splice Machine is open source and is built upon the popular Apache Hadoop, HBase, and Spark distributed platforms. Companies in financial services, healthcare, retail, manufacturing and energy deploy Splice Machine to improve their operational efficiency, eliminate unnecessary costs and deliver superior service. The Splice Machine database can be deployed on-premise or as a fully-managed cloud service.
Splice Machine is a trademark of Splice Machine, Inc. All other trademarks are the property of their respective registered owners. Trademark use is for identification only and does not imply sponsorship, affiliation, or endorsement.
SOURCE Splice Machine
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Dr. Pavlo is a member of the Database Group at CMU, a leading research collective that focuses on database systems, data mining and machine learning, and the Parallel Data Lab, academia's premiere storage systems research center at Carnegie Mellon University. He is the recipient of a 2018 Sloan Research Fellowship, the 2018 SIGMOD Best Paper award, and several faculty research awards from Google and Facebook. Pavlo has been invited to give talks all over the world, most recently at the Percona Live – Open Source Database Conference, SIGMOD, DataEngConf in NYC, In-Memory Computing Summit, and various universities.
"I'm excited to work with the Splice Machine team to solve some of the biggest database challenges for real-time, data intensive applications," said Pavlo. "Splice Machine is working in an interesting space right now. Splice Machine's data platform provides a scale-out architecture for hybrid transactional and analytical (HTAP) workloads that are important for enterprises looking to leverage machine learning technologies on their operational data sets."
Andy is the quintessential technical advisor. He and his students are breaking new ground in research published at the recent SIGMOD and VLDB conferences but he also has extensive industrial database experience as an early member of the H-Store/VoltDB research team led by Michael Stonebraker.
Splice Machine is a scale-out SQL RDBMS, data warehouse and machine learning platform in one. Since launching in 2012, the Company has helped companies migrate underperforming SQL workloads to the modern scale-out architecture, helped operationalize data lakes (often labeled as data swamps) by making them real-time, and helping enterprises build greenfield AI applications in a fraction of the time, money, and staff it takes to do so in a bespoke manner.
Splice Machine works with companies to build AI applications for use in financial services, healthcare, manufacturing, retail, and more. These intelligent apps can generate lasting business and customer benefits by reducing costs, boosting efficiency, and finding answers to customer's most challenging problems in real time. For example, in healthcare Splice Machine helps predict patient conditions and proactively advises doctors and nurses to save lives. In manufacturing, Splice Machine helps companies predict late orders and demand spikes to optimize supply chains and reduce inventories. Splice Machine also helps companies spot fraud earlier to proactively safeguard business and consumers.
To learn more about Splice Machine and its offerings, please visit http://www.splicemachine.com.
About Splice Machine Splice Machine is a new AI data platform for digital transformation. Unlike other Big Data platforms that provide offline, batch analysis, Splice Machine powers intelligent applications that are woven into the operational workflows of companies. It is a scale-out SQL RDBMS, data warehouse and machine learning platform in one. Splice Machine is open source and is built upon the popular Apache Hadoop, HBase, and Spark distributed platforms. Companies in financial services, healthcare, retail, manufacturing and energy deploy Splice Machine to improve their operational efficiency, eliminate unnecessary costs and deliver superior service. The Splice Machine database can be deployed on-premise or as a fully-managed cloud service.
Splice Machine is a trademark of Splice Machine, Inc. All other trademarks are the property of their respective registered owners. Trademark use is for identification only and does not imply sponsorship, affiliation, or endorsement.
SOURCE Splice Machine
