Special Report: Embracing Digital Transformation 2.0

To work most effectively, digital transformation requires updating technology to ensure that mission-critical applications are able to take advantage of new data sources as well as machine learning to become smarter.

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While cloud migration and containerization are important, real business benefits will come from companies breaking down their data science silos.
Companies need massively scalable infrastructure for combined operational, analytical, and ML workloads. For Hadoop to succeed, widespread pre-integration is essential.
Organizations need to think hard about the risks before replacing the legacy database that underlies their custom applications with a NoSQL database.
Apache Hadoop emerged on the IT scene in 2006 with the promise to provide organizations with the capability to store an unprecedented volume of data using commodity hardware. Its promise and early traction may have been too much of a good thing.
How to identify the in-the-moment decisions for your organization and make them a part of your mission-critical business process.

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About Splice Machine

Splice Machine is an Operational AI Platform that unlike relational databases and Hadoop distributions is scalable, real-time, easy-to-use, and continuously learns. It combines the functionality of an operational database (RDBMS), an analytical database (OLAP) and a machine learning workbench (ML) in one unified platform. Splice Machine can be deployed on-premises or in the cloud and is built on open source technology.