The course explores the architecture and implementation of data-driven computing architectures. Topics include the evolution of database technologies, SQL vs. NoSQL, time series databases, and the difference between data lakes, data warehouses, and data lakehouses. Students will engage in hands-on exercises using relevant technologies such as Apache Spark and Databricks to build data pipelines. The course also covers metadata management and governance in maintaining robust and scalable databases.
Please see more information in the Study Guide.
For the course to be arranged there is a minimum limit of 6 local students, which will be confirmed on January 7.
By the end of this course, students will be able to:
– Design and implement data pipelines using relevant technologies.
– Differentiate between data lakes, data warehouses, and data lakehouse systems.
– Apply principles of metadata management and governance in data lakehouses.
– Integrate various data storage and processing frameworks to build scalable and cost-effective data solutions that meet the demands of data-driven organizations.
General IT knowledge, a basic understanding of database systems, distributed computing, and experience with scripting in Python or another programming language.
Course material provided on Moodle site.
The course consists of lectures (hybrid = online and on site), guest lectures, exercises, and group projects.
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