AI in data engineering: Use cases, benefits, and challenges

AI is transforming data engineering by automating tasks such as code generation, reviews, data migrations, and warehouse optimization, allowing engineers to work more efficiently and focus on higher-impact tasks. These advancements promise significant time and cost savings while fostering innovation and accessibility across data teams. However, challenges such as data security, organizational readiness, and maintaining high data quality must be addressed to fully leverage AI's potential in data engineering workflows. As AI tools evolve, they offer the potential for increased efficiency and scalability, although careful consideration is needed to ensure successful implementation and integration into existing systems.