With the world changing rapidly the data and the system that controls the data are also changing. At the beginning of the Internet and WWW, the data used to be stored on one’s personal machine, and it was accessed by only the person owning the device.
But now, with thousands of companies working with millions of employees and billions of terabytes of data, it is necessary to control and monitor who is going to access the data and who is going to made changes in it.
Facebook and Twitter have left most other companies around the world far behind when it comes to using machine learning to improve their business model. And while their practices haven’t always resulted in the best reactions from end-users, there’s much to be learned from these companies on what to do–and what not to do–when it comes to scaling and applying data analytics.
While Facebook seemingly uses machine learning for everything — it is used for content detection and content integrity, sentiment analysis, speech recognition, and fraudulent account detection, as well as operating functions like facial recognition, language translation, and content search functions. The Facebook algorithm manages all this while offloading some computation to edge devices in order to reduce latency.
In recent years, NoSQL distributed databases have become common, as they are built from the ground up to be distributed. Yet they force difficult design choices such as choosing availability over consistency, data integrity, and ease of query to meet their applications’ need for scale. This Refcard serves as a reference to the key characteristics of distributed SQL databases and provides information on the benefits of these databases, as well as insights into query design and execution.
Source de l’article sur DZONE
