Welcome to the Cloud Data Engineering course, the third installment in the Building Cloud Computing Solutions at Scale Specialization. This course provides a deep dive into applying Data Engineering to real-world projects using cloud computing concepts. It is designed for beginners and intermediate students interested in leveraging cloud computing for data science, machine learning, and data engineering. The course equips learners with the skills to develop data engineering applications, apply software development best practices, and utilize cloud-native technologies to tackle complex data engineering solutions.
Throughout the course, students will explore various modules, including getting started with cloud data engineering, examining principles of data engineering, building data engineering pipelines, and applying key data engineering tasks. Additionally, learners will develop a serverless data engineering pipeline in a Cloud platform such as Amazon Web Services (AWS), Azure, or Google Cloud Platform (GCP) as a part of their project.
This course is an excellent opportunity for individuals seeking to expand their knowledge in cloud computing and data engineering, enabling them to contribute effectively to modern technology-driven businesses.
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Cloud Data Engineering is a comprehensive course encompassing modules on getting started with cloud data engineering, principles of data engineering, building data engineering pipelines, and applying key data engineering tasks. Students will gain expertise in cloud-native technologies, data engineering applications, and best software development practices.
Getting Started with Cloud Data Engineering module provides an introduction to cloud data engineering, the end of Moore's Law, distributed systems, big data, and high-performance code creation. It covers various concepts such as elasticity, data feedback loop, and challenges in distributed systems and big data platforms.
Examining Principles of Data Engineering module delves into the principles of data engineering, including batch vs. streaming vs. events, building CLI tools with Click, advanced testing with Amazon CodeGuru and AWS CodeBuild, and mapping functions to CLI. Students will also explore the power of events, containerized CLI, and advanced code analysis tools.
Building Data Engineering Pipelines module introduces students to serverless data engineering, serverless concepts, AWS Lambda, AWS Cloud9, and data governance. Additionally, it covers cloud security with IAM on AWS, AWS shared security model, and encrypting at rest and transit. Students will work on building a serverless data pipeline and serverless tools using a CLI.
Applying Key Data Engineering Tasks module focuses on ETL, cloud databases, cloud storage, and serverless data engineering pipeline creation. Students will explore real-world problems in ETL, cloud storage solutions, and disaster recovery with Amazon S3. They will also gain insights into utilizing cloud databases such as Google BigQuery, AWS Aurora Serverless, AWS DynamoDB, and AWS Redshift.
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