Veidlapa Nr. M-3 (8)
Study Course Description
Data Engineering
Course Code
SZF_174
Branch of Science
-
ECTS
6.00
Target Audience
Business Management; Information and Communication Science; Management Science
LQF
Level 7
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Linda Alksne
Structure Unit Manager
Ieva Puzo
Structural Unit
Faculty of Social Sciences
Contacts
Dzirciema street 16, Rīga, szf@rsu.lv
About Study Course
Objective
This course aims to provide business and project managers with an understanding of the fundamentals of data engineering and its importance in modern business. As part of the course, participants will gain knowledge about data flow and data processing processes, which will help them plan and manage projects that use data more successfully, as well as understand the requirements and challenges in creating and maintaining data infrastructure.
Preliminary Knowledge
In order to successfully participate in this data engineering course, participants should have a basic understanding of computer science and IT infrastructure, as well as basic knowledge of databases and data analysis. An understanding of business processes and how data is used to make decisions would also be helpful. Knowledge of project management to better oversee and coordinate data projects from a business perspective will be an advantage.
Study Course Theme Plan
FULL-TIME
Part 1
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Data Engineer Role and Responsibilities
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Data pipelines
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Data pipelines
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Data storage systems and databases.
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Data storage systems and databases.
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Data storage systems and databases.
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Batch VS Streaming data processing, telemetry and IoT data
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Batch VS Streaming data processing, telemetry and IoT data
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Batch VS Streaming data processing, telemetry and IoT data
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Big data processing, distributed computing (Spark, Hadoop)
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Big data processing, distributed computing (Spark, Hadoop)
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Big data processing, distributed computing (Spark, Hadoop)
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Clod computing (AWS, Google Cloud, Azure)
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Clod computing (AWS, Google Cloud, Azure)
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Lecture
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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Data integration and data quality assurance
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Data integration and data quality assurance
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Data Processing Ecosystem
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Design and architecture of data warehouses
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Design and architecture of data warehouses
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Lecture
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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Data lake structures and best practices
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Data lake structures and best practices
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Real-time data processing
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Data engineering project management
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Auditorium
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2
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Topics
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Data engineering project management
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Total ECTS (Creditpoints):
6.00
Contact hours:
48 Academic Hours
Final Examination:
Exam
Bibliography
Required Reading
1.
Kleppmann M. 2017. Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable SystemsSuitable for English stream
2.
Akidau T., Chernyak S., Lax R. 2018. Streaming Systems: The What, Where, When, and How of Large-Scale Data ProcessingSuitable for English stream
3.
Dutt D.G. 2019. Cloud Native Data Center NetworkingSuitable for English stream
4.
Akerkar R. 2014. Big Data: Principles and Paradigms (akceptējams izdevums)Suitable for English stream
5.
Krishnan K. 2013. Data Warehousing in the Age of Big Data (akceptējams izdevums)Suitable for English stream
Additional Reading
1.
Glass R., Callahan S. 2014. The Big Data-Driven Business: How to Use Big Data to Win Customers, Beat Competitors, and Boost ProfitsSuitable for English stream
2.
Shalender K., Singla B., Singh N., Singla R. 2025. Integrating AI with Data Science: Realising Full Potential of Data-driven Decision Making. Navigating Data Science in the Age of AI: Exploring Possibilities of Generative Intelligence, pp. 1 - 11Suitable for English stream