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A “no compromises” programming language for data scientists, Julia combines the ease of a dynamic language with the speed of a compiled language
The pitfalls and benefits of using synthetic data to train AI algorithms
Premature, untested software, criminal infiltration, multiple technology variations, and lack of experience are just some reasons to distrust distributed hyperledgers in your business
With so much information flowing through APIs, the API management system offers a central hub for business insight
Data architects and leaders are often tasked with collecting a wide range of new data. Sometimes that can lead to the inadvertent growth of new data stores
These three mistakes are costly, but they are also easily avoidable if you know what to look for
AWS seems to be building natural bridges between on-premises databases like MySQL and cloud services like Amazon Aurora
That’s good for everyone, as it respects that data has become so important to people’s livelihood—their credit scores just as much their personalities—that it shouldn’t be treated differently than they would be treated
A step-by-step guide to using SAN-less failover clustering software to support SQL Server’s Always On Failover Clustering in the public cloud
Traditional business methods aren’t enough to help businesses stay competitive in today’s data-driven world. Data science outperforms these outdated methods, especially in certain key areas