There's a common misconception that in order to run machine learning and other algorithms on data, you need to be working within a specific data science platform like TIBCO or Alteryx. While these platforms certainly come with specialized benefits, there are certain use cases where it's possible, not to mention easier and more cost effective, to do machine learning inside a database. We actually recently hosted a Python and Machine Learning livestream, where Google Engineer and Startup Founder, Joel Wasserman, was able to use a database to build a Python app and run a machine learning model on the data to predict whether it's safe to go skydiving. I highly recommended checking out Joel's demo at the link, but in the meantime, let's explore the theory in question. This article does a great job of summarizing what a good Data Science and Machine-Learning Platform should have.

HarperDB was named after CEO Stephen Goldberg's dog, a five-year old adopted pup.HarperDB The so-called Internet of Things (IoT) is growing, exponentially, obviously. As an example of the machines that populate the IoT, modern aircraft are estimated to now fly with connected sensors monitoring as many as 5000 component elements per engine every second - and that's just the engines. For equipment engineers in aviation (and every other industry now digitally transforming) this means a lot of head scratching, some cool innovations and a lot of fine-grained physical tuning with a fair dose of engine grease. The same challenge also exists for information technologists supporting these systems. For software programmers and database engineers in every industry, making the IoT work means a lot of brain-aches, some super-cool innovations and a lot of fine-grained keyboard and screen based tuning, with a fair dose of'virtual' microprocessor engine grease (spoiler alert: microprocessors are built in clean room labs and rarely get oiled with lubricant).