The Data Standard
In this episode, Simon Aloyts talks about the challenges of handling non-relational data and big data. He discusses the problems companies face when trying to cope with massive volumes of data and the need for new methods of data management.
He talks about how data analytics has to change to cope with the massive volumes of generated data. Simon explains that the current tools can handle only parts of the generated data and that data scientists have to combine multiple tools to extract the data they need. The time for rows and columns is long gone, so scientists have to think outside of the box to get the results they need.
He explains how a single link generates massive amounts of data and how hard it is to sort everything out so you can draw conclusions that help provide a better user experience. Simon talks about cloud technology and how it makes data extraction easier as businesses don’t have to develop their own solutions. He thinks that cloud technologies are the only available tech with the power necessary to analyze data and extract useful information
Meet The Host
Data scientist at The Data Standard
Catherine Tao is a tech enthusiast looking for new methods for building connections with businesses around the world. Her extensive knowledge of data science allowed her to develop new solutions and implement them into existing ecosystems. She is currently working as a Data scientist and Exclusive Podcast Producer at The Data Standard.
Meet The Guest
Vice President of Cloud and Data Engineering at Itransion Group
mon Aloyts has worked for multiple IT leaders, including Microsoft, multiple startups, and engineering projects. His knowledge extends from data analytics to cloud computing and engineering.