As the data industry evolves, data insights become increasingly accessible, forming the fundamental elements for various time-centric applications such as fraud detection, anomaly detection, and business insights. An essential and widely recognized component in unlocking the value and accessibility of this data is comprehending its nature and interrelationships and extracting meaningful values.
Data modeling serves as the crucial blueprint akin to architectural plans for a house, defining how data is structured, modeled, and interconnected to facilitate informed decision-making efficiently. Additionally, data modeling elevates raw data collection to a level that is ready to be transformed into information, empowering the users to derive valuable insights. This article highlights the significance of data modeling, data storage, and ETL (Extract, Transform, Load) design tailored for downstream analytics. It draws insights from sample data obtained through the Binance crypto open API.