Stephen Foster's Blog

Data integration refers to consolidating data from different sources, making it useful for analyzing and getting business intelligence. Being crucial for data management, it incorporates data discovery, ingestion, mapping, cleansing, transforming, and delivery of data from varied sources. Integrating data into a specific format reduces its complexity and makes it more useful and easily accessible. It enables managers to gain valuable insights that help in taking crucial business decisions. Experienced data engineers of a leading data engineering company adopt various data integration patterns to standardize the overall process, based on the business needs and use cases. Check out five data integration patterns that data engineers use to move, transform, and consolidate data and make it more valuable to businesses.  Aggregation Aggregation data integration pattern involves retrieving data from multiple sources, merging them into one system, and transforming them to provide a real-time