Coursera
4 weeks to complete at 10 hours a week
In this course, you will learn how to apply Data Engineering to real-world projects using Cloud computing concepts, including developing Data Engineering applications, using software development best practices, and leveraging Cloud-native technologies.
Students should have beginner level Linux and intermediate level Python skills.
The course covers topics such as the end of Moore's Law, developing distributed systems, best practices for Big Data processing, serverless data engineering techniques, and data governance principles. You will apply these concepts by building projects like a GPU programming project, a command-line data processing tool, a serverless data engineering pipeline, and a serverless AWS Lambda function that labels images using the AWS Rekognition API.
This course is part of the Building Cloud Computing Solutions at Scale Specialization offered by Coursera, which is taught by industry experts and provides hands-on projects to help you develop practical cloud computing skills.
After completing this course, you will be able to develop data engineering applications and use software development best practices to create data engineering solutions, including continuous deployment, code quality tools, logging, instrumentation, and monitoring. You will also be able to leverage Cloud-native technologies to tackle complex data engineering problems.
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