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‍We are in an era of unprecedented innovation in databases. Data-intensive companies are grappling with whether the many new options — NoSQL, Key-Value, Document, Column Family, Column-Oriented — are appropriate for them. The commercial success of Facebook and LinkedIn makes graph databases a hot area of investigation. Unlike many new databases, they are not a variation on or a simplification of relational databases. Instead they require new ways of thinking and modeling data. In return they can answer truly novel questions.

Today I had the lovely experience of being told “the network to the cluster is down” while I was writing some code that was supposed to use the cluster. Was I stalled? How could I test my logic? It turns out we’re rather obsessive about separating interface from implementation, usually via C# interface definitions. In this case, I just went down the road I was going down anyway: making some simple mock objects to model the cluster dependencies. (We use Moq.) Now I don’t really care that the network is down.

Identify ROI and business case costs at a high level. Increase delivery with real-time impact and transport analysis.Learn how Graph Technology + Machine Learning will improve delivery times, minimize transport risk through data analysis and increase product satisfaction. Use predictive analytics to reduce operational bottle necks and discover areas of concern affecting supply. Using Graph + ML allows you to increase customer satisfaction through visualization of supply and demand impacts.