Duke University
2 weeks to complete at 10 hours a week
This advanced data engineering course will teach you how to scale your data systems using cutting-edge tools like RabbitMQ, Apache Airflow, and vector/graph databases. You'll gain practical expertise in building efficient, high-performance data pipelines and workflows to handle large, complex datasets.
This course is designed for data scientists, data engineers, and anyone with a foundational understanding of data handling who wants to escalate their skills to work with larger, more complex datasets. You should have familiarity with programming, preferably with Python, as well as experience with version control systems, the terminal, and running shell commands.
This course covers a range of advanced data engineering topics, including: - Scalable data consumption using Celery and RabbitMQ - Optimized workflow management with Apache Airflow - Robust data storage and management at scale using vector and graph databases - Hands-on projects to apply your skills in solving real-world data engineering challenges - Performance analysis and optimization of data systems
Duke University is a world-class institution with a strong commitment to applying knowledge in service to society. The instructors for this course, Noah Gift and Alfredo Deza, are experienced data engineering experts who have designed this program to provide learners with practical, industry-relevant skills to excel in the field of advanced data engineering.
Upon completion of this course, you will be equipped with the skills and knowledge to tackle complex data engineering challenges, build scalable data pipelines and workflows, and optimize data systems for high performance. This will prepare you for in-demand roles such as Senior Data Engineer, Big Data Engineer, Data Architect, and Data Infrastructure Specialist.
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