Backfilling is a critical process in data engineering that ensures the completeness and reliability of datasets by addressing gaps in historical data. It's essential for maintaining accurate analytics, compliance, and business decision-making. Common challenges in backfilling include handling large data volumes, ensuring data consistency, resource and time constraints, risk of errors, and lack of observability and monitoring. To overcome these challenges, organizations can implement best practices such as planning and documenting the process, validating and cleaning data first, automating the backfilling process, monitoring progress in real-time, using data version control, testing before deploying changes, and optimizing data backfilling with tools like Acceldata. By adopting these strategies and leveraging modern solutions, organizations can enhance their data pipelines, maintain data quality, and drive informed decision-making.