Mastering Generative AI for Data Engineering

Generative AI skills are in demand, with Ascend.io reporting that 89% of respondents reporting using generative AI in some capacity. ZipRecruiter reports that data engineers with generative AI skills earn an average of USD 115,000 annually, with top-end salaries of USD 179,000 annually. Data engineering is responsible for building strong data pipelines, managing data infrastructure, and ensuring high-quality data evaluation. This course is suitable for existing and aspiring data engineers, data warehousing specialists, and other data professionals such as data analysts, data scientists, and BI analysts. First, learn about the current impact of generative AI on data engineering. Throughout the course, you will assume the role of a data engineer and gain experience using generative AI to enhance productivity by introducing innovative ways to deliver projects. You will learn how to use and apply generative models for tasks such as architecture design, database querying, data warehouse schema design, data augmentation, data pipelines, ETL workflows, data analysis and mining, data lake house, and data repositories. You will also explore challenges and ethical considerations associated with using Generative AI. To complete this course, you'll demonstrate your new generative AI skills in a real-world, shareable, hands-on data engineering project. After you successfully complete your final quiz, you will receive your certificate, and you can request your Badge. You can share both your project and certificate with your current or prospective employers.

• Use generative AI tools and techniques in data engineering processes across industries
• Apply generative AI solutions for data generation, augmentation, and anonymization
• Evaluate real-world case studies that feature successful application of Generative AI for ETL and data repositories
• Build generative AI skills in hands-on labs and projects for data warehouse schema design and infrastructure setup

• Fundamentals of generative AI, prompt engineering, and data engineering