In scenarios where marketing teams need to analyze vast amounts of data quickly, relying solely on Postgres for both operational and analytical workloads can lead to significant slowdowns due to its row-based storage limitations. While Postgres excels in handling transactional operations with features like ACID compliance and multi-version concurrency control, it struggles with large-scale analytical queries, which can hinder real-time operations. To address this challenge, replicating data to a dedicated analytical warehouse like Amazon Redshift is recommended, as it is optimized for large-scale analysis and integrates seamlessly with AWS services. Tools such as AWS Database Migration Service, Fivetran, and Redshift Data Sharing facilitate this replication, ensuring that analytical workloads are efficiently managed without impacting the performance of the production database. By utilizing Redshift, companies can execute extensive analytical queries without disrupting daily operations, effectively transforming raw data into valuable insights while maintaining the stability and speed of Postgres for transactional processes.