In data-intensive systems, there are concepts and principles which are universal for almost all projects. Data management usually spans over all stages of the data processing lifecycle including working in an interdisciplinary way to meet the diverse requirements of the end-users. In this course, students learn the meaning of each step of the data processing lifecycle and are enabled to use suitable techniques for each of these steps. In data ingestion and integration, students learn principles and techniques which enable them to ingest and integrate heterogeneous data from various data sources. As the core of most data management projects, students will learn data processing and storage techniques. To build data-intensive systems in an effective and goal-oriented way, students are presented with principles and techniques for data analysis and reporting. As storing and processing data becomes more prevalent in organizations as well as in everyday life, students learn about ethics in data handling in varying cultural contexts and about complying with legal regulations and corresponding techniques. As far as scalability and reliability is concerned, students learn how distributing data across multiple machines can increase the performance of the system and the resilience and ease of recovery from failovers. With respect to organizing data for interdisciplinary usages in organizations and businesses, students learn principles and techniques for data governance, data modeling and data quality. Finally, students are enabled to understand different types of metadata and their respective meaning and organization in data management projects.

Storing and managing data in databases is at the very heart of data management. In this course, students learn about concepts and technologies for the management and usage of databases. Students are provided with an in-depth look into the concepts and inner workings of databases and their major components. Students learn to differentiate between different categories of databases, and are enabled to understand and use database principles and technologies such as the ACID principle, the differentiation between OLAP and OLTP systems, indexing strategies and industry standards for connecting to databases. As modern systems tend to increase in data volume, students learn how distributing databases across clusters of machines can increase the scalability and reliability of data systems. Students learn concepts and techniques for distributing data across clusters, such as fragmentation and sharding, as well as the challenges and strategic decisions to be made within this context, such as the compliance with consistency levels. With focus on databases, students learn about data protection and security, ethics and principles, as well as practical techniques to facilitate the compliance with these principles under varying cultural contexts and perspectives. Finally, students are enabled to differentiate between common databases and learn how to practically perform common database tasks in each respective database.

The usefulness of relational SQL databases has been proven by their universal distribution and diverse applications. In some aspects, however, relational SQL databases do not meet the requirements of modern applications in terms of, for instance, flexibility and cardinality. This gave birth to a family of database concepts which became known as NoSQL databases. In this course, students learn how traditional SQL databases are different from these NoSQL databases which usually, and as one of the most noticeable characteristics, do not enforce a data schema on write. Students acquire a thorough understanding of the concepts of NoSQL databases and learn how to evaluate the suitability of various NoSQL databases for specific data-intensive projects. Students are enabled to explain the main concepts of Key-Value-oriented, Document-oriented, Column-oriented and Graph-oriented Databases and will be provided with applied examples for each of these database types. Finally, students learn how to practically use these databases in specific problem-oriented use cases.