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.
Resources
Blog
While cloud migration and containerization are important, real business benefits will come from companies breaking down their data science silos.
Blog
Companies need massively scalable infrastructure for combined operational, analytical, and ML workloads. For Hadoop to succeed, widespread pre-integration is essential.
Blog
Organizations need to think hard about the risks before replacing the legacy database that underlies their custom applications with a NoSQL database.
Blog
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.
Blog
How to identify the in-the-moment decisions for your organization and make them a part of your mission-critical business process.
Featured Resources
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In this webinar, Splice Machine CEO Monte Zweben explains how to modernize and scale your SQL applications, making them agile and intelligent – without rewriting them. Watch Now -
To fully realize the promise of the current second digital transformation, businesses need to modernize their custom-built applications to be enriched with new sources of data and “intelligence.”
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Today’s digital economy requires businesses develop fast, responsive technology that will support their efforts. That’s why application development has evolved into a collaborative, ongoing process that involves constant interaction between technology and business teams.
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The insurance industry has a renewed realization of the value of data due to AI and machine learning. Five takeaways on the trends, challenges, and opportunities.
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What to avoid in 2020 when moving to the cloud, modernizing custom applications, and investing in data science initiatives.
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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.