In todays world data is growing really fast across many different platforms. This includes things like systems and APIs as well as cloud-based flat files. Because of this we need to create flexible data engineering frameworks. My research presents an implementation of an end-to-end data pipeline. This pipeline is specifically designed for the Healthcare Revenue Cycle Management domain. One big problem we are trying to solve is that old systems are not flexible or automated enough. They often make mistakes when handling amounts of sensitive financial and patient data. These old systems can also slow down. Create separate groups of data that make it hard to make good decisions. My solution uses a set of Microsoft Azure services. These services help move enterprise data from systems to a new cloud-based system. This is done in an seamless way. The main part of this architecture is Azure Data Factory, which is used to manage and move data. This creates an encrypted bridge for data to move so sensitive healthcare information is never exposed to the public internet. Data is stored in Azure Data Lake Storage Gen2, where it is managed in a way. This includes three layers: Bronze, Silver and Gold. For data transformation and quality assurance the system uses Azure Databricks with PySpark. This layer does jobs like removing duplicates handling missing values and standardizing formats. This ensures that the data is very accurate. Security and governance are very important. Azure Key Vault is used to manage credentials and Role-Based Access Control is used to make sure we follow healthcare rules. The results of this project show that it is more reliable and needs less manual work. By moving to this cloud-based system organizations can get rid of the limitations of systems. They can also create a foundation for advanced reporting, business intelligence and future machine learning projects. This project provides a plan for large-scale data engineering solutions in environments. The Healthcare Revenue Cycle Management domain will benefit from this research. The Microsoft Azure services used in this project are very important. The Azure Data Factory and Azure Databricks are components of this project. The results of this implementation are very promising. The Healthcare Revenue Cycle Management domain can use this project as a model, for their data engineering solutions.
Microsoft Azure, Data Engineering, Healthcare RCM, Medallion Architecture, Azure Data Factory, Cloud Migration, PySpark
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