What Does MVP Stand For? It’s Not What You Think.
October 7, 2024
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This course is part of Building Cloud Computing Solutions at Scale Specialization
Instructor: Noah Gift
12,933 already enrolled
Included with
(80 reviews)
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Intermediate level
Beginner level Linux and Python skills
(80 reviews)
Recommended experience
Intermediate level
Beginner level Linux and Python skills
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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).
This week, you will learn about the methodologies involved in Data Engineering. You will also learn to evaluate best practices for dealing with the end of Moore’s Law, develop distributed systems that apply software engineering best practices and evaluate best practices for implementing solutions with Big Data. You will apply these practices to build a GPU programming project using Numba and the CUDA SDK.
25 videos22 readings5 assignments4 discussion prompts3 ungraded labs
This week, you will learn what Data Engineering is and how to use software engineering best practices in Data Engineering. You will then apply this knowledge by building a command-line data processing tool.
16 videos10 readings4 assignments3 discussion prompts4 ungraded labs
This week, you will learn serverless data engineering techniques and data governance best practices. You will then apply this knowledge by building a serverless Data Engineering system.
23 videos7 readings3 assignments2 discussion prompts
This week, you will learn about key Data Engineering tasks including ETL, Cloud Databases and Cloud Storage. You will then apply this knowledge by building a serverless AWS lambda function that labels an image using the AWS Rekognition API.
20 videos10 readings4 assignments3 discussion prompts4 ungraded labs
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
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Reviewed on Dec 30, 2021
Very important information and concepts was shared.
Reviewed on Dec 20, 2022
Great introduction to the concepts, very good practical examples, with relevant technologies and platforms.
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