AWS Academy Data Engineering is designed to help students learn about and get hands-on practice with the tasks, tools, and strategies that are used to collect, store, prepare, analyze, and visualize data for use in analytics and machine learning (ML) applications. Throughout the course, students will explore use cases from real-world applications, which will enable them to make informed decisions while building data pipelines for their applications.
FAQs
Is this course replacing AWS Academy Data Analytics?
Yes. The reason for this change is that a data engineer's primary job is to prepare data for analytical or operational uses, while data analytics is more focused on analyzing data that has already been prepared
What is a data engineer?
A data engineer is an IT worker whose primary job is to prepare data for analytical or operational uses.
Course Objectives
- Illustrate a data pipeline by using AWS services to meet a generalized use case.
- Design and build a data collection process while considering constraints such as scalability, cost, fault tolerance, and latency.
- Implement the steps to process structured, semi-structured, and unstructured data formats in a data pipeline that is built with AWS.
- Identify the risks and approaches to secure and govern data at each step and each transition of the data pipeline.
- Select a data storage option that matches the requirements and constraints of a given data analytics use case.
- Differentiate the characteristics of a machine learning pipeline and its specific processing steps.
Prerequisites
This course requires a strong foundation in IT concepts and skills. To ensure success in this course, students should have the following:
- Completed the AWS Academy Cloud Foundations course or have equivalent experience.
- Worked with Structured Query Language (SQL).
- Worked with databases.
- Introduced to general networking concepts.
- Understanding of decision-making knowledge in math, probability, and statistics.
Instructor
TBD
Registration and Enrollment
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