Data-intensive Systems and Engineering (DAT535)

Content

The emergence of Big Data and Data-intensive Systems as specialized fields in computing has been motivating development of new techniques and technologies needed to extract knowledge from large datasets. Since Hadoop was conceived in 2005, popular interest in data-intensive systems began to grow. It resulted - over time - in a collection of technologies, methodologies, and practices to cover the complete data lifecycle.

This course is a first step to a variety of roles related to data-intensive systems. The core tasks in these roles that we will address are: roles in a data team, data acquisition and integration (using files, APIs, etc.), data cleaning and augumentation (often using direct implementation of MapReduce jobs), data analytics and ML (often using one of data processing frameworks e.g. SparkSQL, MLlib), advocating technology application both in technical and non-technical setting, providing introductory training to coworkers.

Learning outcome

Knowledge

• Understanding of Medallion Architecture: Students will gain a comprehensive understanding of the Medallion Architecture, including its layers (bronze, silver, and gold) and how it supports data processing and analytics.
• Apache Spark Fundamentals: Students will learn the core concepts of Apache Spark, including its architecture, components, and how it handles big data processing.
• Data Management and Governance: Knowledge of data management principles, data governance, and best practices for ensuring data quality and integrity.
• Big Data Ecosystem: Familiarity with the broader big data ecosystem, including tools and technologies that complement Apache Spark, such as Hadoop, Kafka, Delta Lake, NOSQL databases.

Skills

• Data Processing and Transformation: Proficiency in using Apache Spark for data processing tasks, including batch and stream processing, data cleaning, and transformation.
• Performance Tuning: Skills in optimizing Apache Spark jobs for performance, including resource management, partitioning, and tuning Spark configurations.
• Data Integration: Competence in integrating data from various sources and formats into a unified data platform using Medallion Architecture principles.
• Problem-Solving: Ability to troubleshoot and resolve issues related to data pipelines, data quality, and performance bottlenecks.

General qualifications:

• Collaboration and Communication: Effective communication and collaboration skills to work with cross-functional teams implementing data-intensive solutions.
• Ethical Considerations: Awareness of ethical considerations in data engineering, including data privacy, security, and responsible data usage.

Required prerequisite knowledge

Recommended prerequisites

Bash programming

Administration of Cloud and container-based environments

Databases, SQL

Exam

Coursework requirements

Three assignments

Students start with 3 mandatory assignments that contain programing and system administration. Assignments are to be completed individually. All mandatory assignments must be passed within deadline so that the student has the right to start with the project. The obligatory assignments give access to the project only in the current semester.

Completion of mandatory lab assignments is to be made at the times and in the groups that are assigned and published. Absence due to illness or for other reasons must be communicated as soon as possible to the laboratory personnel. One cannot expect that provisions for completion of the lab assignments at other times are made unless prior arrangements with the laboratory personnel have been agreed upon.

All group members must participate in the project presentation.

Course teacher(s)

Course coordinator:

Laboratory Engineer:

Head of Department:

Method of work

Open for

Course assessment

Literature