Data engineering faces challenges like handling large datasets, ensuring data quality, and maintaining system scalability. Overcoming these requires adopting distributed computing frameworks like Apache Spark for big data processing. Implementing data governance and validation ensures accuracy. Automating pipelines with tools like Apache Airflow enhances efficiency. Cloud-based storage provides scalability, while monitoring solutions track data integrity. Collaboration between data engineers and business stakeholders improves alignment. Addressing these challenges with modern tools and best practices ensures robust data systems that support advanced analytics and business intelligence.
