Data engineers often face challenges in maintaining high query performance and managing heavy workloads on platforms like Postgres, which can lead to bottlenecks in storage and scaling, especially when large-scale analytics are required. By replicating data to a specialized analytics system such as BigQuery, teams can alleviate these issues, allowing Postgres to focus on transactions and data integrity while BigQuery handles extensive analytical tasks. This combination leverages the strengths of both platforms: Postgres excels in managing structured data with high-speed transactions, whereas BigQuery offers efficient, serverless querying for large datasets, thus enhancing operational data analysis without straining production systems. The integration of Postgres and BigQuery provides an effective solution for balancing transactional workloads with analytical needs, ensuring performance and scalability across data operations.