263-3010-00L Big Data
|Semester||Autumn Semester 2016|
|Periodicity||yearly recurring course|
|Language of instruction||English|
|Abstract||The key challenge of the information society is to turn data into information, information into knowledge, knowledge into value. This has become increasingly complex. Data comes in larger volumes, diverse shapes, from different sources. Data is more heterogeneous and less structured than forty years ago. Nevertheless, it still needs to be processed fast, with support for complex operations.|
|Objective||This combination of requirements, together with the technologies that have emerged in order to address them, is typically referred to as "Big Data." This revolution has led to a completely new way to do business, e.g., develop new products and business models, but also to do science -- which is sometimes referred to as data-driven science or the "fourth paradigm".|
Unfortunately, the quantity of data produced and available -- now in the Zettabyte range (that's 21 zeros) per year -- keeps growing faster than our ability to process it. Hence, new architectures and approaches for processing it were and are still needed. Harnessing them must involve a deep understanding of data not only in the large, but also in the small.
The field of databases evolves at a fast pace. In order to be prepared, to the extent possible, to the (r)evolutions that will take place in the next few decades, the emphasis of the lecture will be on the paradigms and core design ideas, while today's technologies will serve as supporting illustrations thereof.
After visiting this lecture, you should have gained an overview and understanding of the Big Data landscape, which is the basis on which one can make informed decisions, i.e., pick and orchestrate the relevant technologies together for addressing each business use case efficiently and consistently.
|Content||This course gives an overview of database technologies and of the most important database design principles that lay the foundations of the Big Data universe. The material is organized along three axes: data in the large, data in the small, data in the very small. A broad range of aspects is covered with a focus on how they fit all together in the big picture of the Big Data ecosystem.|
- physical storage (HDFS, S3)
- logical storage (key-value stores, document stores, column stores, key-value stores, data warehouses)
- data formats and syntaxes (XML, JSON, CSV, XBRL)
- data shapes and models (tables, trees, graphs, cubes)
- an overview of programming languages with a focus on their type systems (SQL, XQuery, MDX)
- the most important query paradigms (selection, projection, joining, grouping, ordering, windowing)
- paradigms for parallel processing (MapReduce) and technologies (Hadoop, Spark)
- optimization techniques (functional and declarative paradigms, query plans, rewrites, indexing)
We will also host two guest lectures to get insights from the industry: UBS and Google.
Large scale analytics and machine learning are outside of the scope of this course.
|Literature||Papers from scientific conferences and journals. References will be given as part of the course material during the semester.|
|Performance assessment information (valid until the course unit is held again)|
|Performance assessment as a semester course|
|ECTS credits||6 credits|
|Language of examination||English|
|Repetition||The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.|
|Mode of examination||written 90 minutes|
|This information can be updated until the beginning of the semester; information on the examination timetable is binding.|
|Only public learning materials are listed.|
|263-3010-00 V||Big Data||2 hrs|
|263-3010-00 U||Big Data||2 hrs|
|263-3010-00 A||Big Data|
Individual work to get hands-on experience with the technologies covered, no fixed presence required.
|1 hrs||G. Fourny|
|No information on groups available.|
|There are no additional restrictions for the registration.|