Practice session materials for the University of Tartu Data Engineering course, Fall 2026. The course covers data collection, relational and dimensional modeling, transformation, workflow orchestration, and visualization. Students apply these skills in a group project that develops a complete data engineering product.
This repository provides practice instructions, Docker Compose environments, sample datasets, SQL and Python examples, and reference solutions.
- Course overview
- Lecture and practice schedule
- Project requirements and assessment
- Moodle: lecture recordings, quizzes, submissions, and course announcements.
Use the weekly inventory to find practice instructions and prepare for class. Weeks follow the 2026 course schedule; folder numbers are lesson identifiers. Update needed marks materials awaiting revision for this year's course or instructions that are missing from the repository.
| Week | Practice date | Session | Materials | Status |
|---|---|---|---|---|
| 1 | 8 Sep 2026 | Docker and PostgreSQL | Docker practice | Ok |
| 2 | 15 Sep 2026 | Relational modeling and ER diagrams | ER practice | Ok |
| 3 | 22 Sep 2026 | Dimensional modeling and star schemas | Star schema practice | Update needed |
| 4 | 29 Sep 2026 | Workflow orchestration with Airflow | Airflow practice | Update needed |
| 5 | 6 Oct 2026 | Data transformation with dbt | dbt practice | Update needed |
| 6 | 13 Oct 2026 | Semi-structured data: MongoDB and Neo4j | MongoDB practice; Neo4j materials missing | Update needed |
| 7 | 20 Oct 2026 | Data visualization: Streamlit and Superset | Superset practice; Streamlit materials missing | Update needed |
| 8 | 27 Oct 2026 | Project work | ||
| 9 | 3 Nov 2026 | Project work | ||
| 10 | 10 Nov 2026 | Project work | ||
| 11 | 17 Nov 2026 | Project work | ||
| 12 | 24 Nov 2026 | Project work | ||
| 13 | 1 Dec 2026 | Project work | ||
| 14 | 8 Dec 2026 | Project work | ||
| 15 | 15 Dec 2026 | Project work | ||
| 16 | 18 Jan 2027 | Project presentations and poster session |
Start with the Docker practice to prepare your environment. Each session's instructions describe the required services, setup commands, and exercises. Run commands from the directory specified in the lesson. Assignment folders include task descriptions and, where provided, reference solutions.
Students should follow the session instructions together with the guidance given in class.
Additional reference exercises from earlier course editions. These topics have no standalone practical session in the 2026 schedule.
| Status | Topic | Repository instructions |
|---|---|---|
| Legacy | ClickHouse: analytical storage, star schemas, and queries | 05_ClickHouse |
| Legacy | Iceberg with DuckDB, PyIceberg, and MinIO | 08_Iceberg |
| Legacy | Security and privacy: permissions, masking, and auditing | 09_Security_Privacy |
| Legacy | OpenMetadata: cataloging, data quality, and lineage | 10_OpenMetadata |
The existing dbt and Superset practices use ClickHouse; the legacy ClickHouse lesson provides supporting setup instructions and sample data.