earticle

영어

Data modeling in data warehousing is a multifaceted process crucial for effective data management and analysis. Through techniques like conceptual, logical, and physical modeling, data architects organize complex data structures. The iterative nature of modeling allows adaptation to evolving business needs. The analysis shows that most organizations used the hybrid approach to create the data modeling in the data warehouse because this approach helps to create a good and efficient model of data in the data warehouse. But on the other hand, when we talk about the anomalies, researchers used an isolation forest and an LSTM algorithm based on net earnings by month. This is the way that helps to remove maximum anomalies from the data warehouse during storing data from sources. In this era, mostly people use Big data concept for data model for data warehousing but this technique is much complex for storing data and retrieval of data in this technique just single thing is missed for the data modeling named as reusability in future working of big data if we majorly emphasize on reusability of data the this tech can be most efficient as compare to present.