Introduction to Data Engineering: what it is, why it's important, and the different areas of focus within the field.
Data Warehousing: concepts, design, and best practices for creating and maintaining a data warehouse.
Data Integration: techniques for extracting, transforming, and loading data from various sources into a data warehouse.
Data Quality: strategies for ensuring the accuracy and completeness of data, as well as methods for identifying and cleaning up any issues.
Big Data: an overview of the challenges and technologies involved in working with large-scale data sets, such as Hadoop and Spark.
Cloud Data Engineering: how to design, build, and manage data pipelines and data lakes in the cloud using AWS, Azure or GCP.
Data Governance and Security: best practices for managing data access and ensuring data security.
Case studies and real-world examples of data engineering projects, to give students hands-on experience and a deeper understanding of the concepts covered in the course.
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