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Data warehouse use has increased significantly in recent years and now plays a fundamental role in many organizations' decision-support processes. A framework that uses parameter sets to define the most suitable synchronization option for a given transaction processing environment helps decrease the update time between the transactional and analytical systems and also reduces the hardware resources required to keep an acceptable data update. The frequency of a data warehouse loading process defines the points of update between the transaction systems and the warehouse with its analytical applications. Normally, data warehouses rely on static updates, with batch loading processes occurring at daily, weekly, monthly, or other periodic intervals. However, today's business needs require an analytical environment that provides (i) continuous data integration with shorter periods for capturing and loading from operational sources, (ii) An active decision engine that can make recommendations, and (iii) high availability. Synchronizing a data warehouse in real time with transactional systems thus requires reducing the interval between update points. To achieve this dynamic option, the analytical database system must immediately reflect updates on transactional data.