This site includes materials for bachelor students from multiple disciplines or practioners to understand basic concepts, techniques, and technologies in big data analytics and platforms.
Even though we try to remove as much as possible difficult, advanced computer science materials in this subject, this basic introduction still requires a good background in computer science to understand. The main content of the lectures (6 hours) is also accompanied with in a set of slides delivered during the lectures. If you want to have deep dive into this subject, we suggest to look at our master level course for big data platforms at Aalto University.
- Learners from business schools: Have your first try with distributed query engines and Dashboards
- Try with Apache Spark
- Streaming analytics
- Using workflows
One type of activities, we have to master is to carry out pipelines to do data ingestion and transformation. One can see that data ingestion and transformation rely on certain types of big data analytics discussed above. However, we can see that data ingestion and transformation are complex applications and workflows that need to combine different techniques.
- Building basic tasks for processing data (e.g., read/write data, basic analysis)
- Using workflows and reactive techniques to build pipelines/workflows
- Applying suitable deployment and management to run ingestion/transformation pipelines
Tools and services atop LLMs are popular for supporting various tasks in big data analytics. Check our basic guide on using LLMs for the study of big data platforms.
Linh Truong, linh.truong@aalto.fi, https://rdsea.github.io