The data revolution is happening now. Outstanding results are achieved using data to predict the future, automate tasks, and visualize the impossible. Indeed, all major players exploit huge amounts of data applying complex algorithms in an industrialized way. Data is flowing from everywhere and storage is affordable. As a consequence, we want to save all data available in our range: social media, IoT devices, sensors, you name it. Considering that, applying schemas to data is becoming hard or sometimes unfeasible: takes time, associated value to data is unknown. Data storage schemes are shifting from data warehouse (schema-on-write approach) to data lakes (schema-on-read). In a nutshell, we store data as is, with minimum formatting and cross-referencing.
Data scientists create algorithms that crunch data to harness business’ value. Many libraries for common inference like TensorFlow, PyTorch, to cite a few, help in this context. However, there is a lack of standards to define applications in this constantly evolving context. Solutions are often ad hoc, and industrialization is done on a case-by-case basis. However, algorithms have many general concepts in common. For instance, grab data from a source API, apply a data frame schema, analyze the data frame, and produce visualization. These algorithms should fit a common standard to be reused, automated, and integrated. This way, a platform can seamlessly adapt itself to any load in order to give data scientists the best user experience possible.
In a nutshell, Ryax was founded with one vision: Data must be treated in the right place, at the right time.