Cloud Data Engineering
Learn how to apply Data Engineering to real-world projects using Cloud computing concepts. Develop Data Engineering applications and use best practices for continuous deployment, code quality, logging, and monitoring. Ideal for beginners and intermediate students interested in Cloud computing for data science, machine learning, and data engineering. Build a serverless data engineering pipeline on AWS, Azure, or GCP.
Welcome to the third course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will learn how to apply Data Engineering to real-world projects using the Cloud computing concepts introduced in the first two courses of this series. By the end of this course, you will be able to develop Data Engineering applications and use software development best practices to create data engineering applications. These will include continuous deployment, code quality tools, logging, instrumentation and monitoring. Finally, you will use Cloud-native technologies to tackle complex data engineering solutions.
This course is ideal for beginners as well as intermediate students interested in applying Cloud computing to data science, machine learning and data engineering. Students should have beginner level Linux and intermediate level Python skills. For your project in this course, you will build a serverless data engineering pipeline in a Cloud platform: Amazon Web Services (AWS), Azure or Google Cloud Platform (GCP).
