252-3900-00L  Big Data for Engineers

Courses

Catalogue data

Abstract	This course is part of the series of database lectures offered to all ETH departments, together with Information Systems for Engineers. It introduces the most recent advances in the database field: how do we scale storage and querying to Petabytes of data, with trillions of records? How do we deal with heterogeneous data sets? How do we deal with alternate data shapes like trees and graphs?
Learning objective	This lesson is complementary with Information Systems for Engineers as they cover different time periods of database history and practices -- you can even take both lectures at the same time. 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. 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. It targets specifically students with a scientific or Engineering, but not Computer Science, background. We take the monolithic, one-machine relational stack from the 1970s, smash it down and rebuild it on top of large clusters: starting with distributed storage, and all the way up to syntax, models, validation, processing, indexing, and querying. 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. No data is harmed during this course, however, please be psychologically prepared that our data may not always be in normal form. - physical storage: distributed file systems (HDFS), object storage(S3), key-value stores - logical storage: document stores (MongoDB), column stores (HBase) - data formats and syntaxes (XML, JSON, RDF, CSV, YAML, protocol buffers, Avro) - data shapes and models (tables, trees) - type systems and schemas: atomic types, structured types (arrays, maps), set-based type systems (?, *, +) - an overview of functional, declarative programming languages across data shapes (SQL, JSONiq) - the most important query paradigms (selection, projection, joining, grouping, ordering, windowing) - paradigms for parallel processing, two-stage (MapReduce) and DAG-based (Spark) - resource management (YARN) - what a data center is made of and why it matters (racks, nodes, ...) - underlying architectures (internal machinery of HDFS, HBase, Spark) - optimization techniques (functional and declarative paradigms, query plans, rewrites, indexing) - applications. Large scale analytics and machine learning are outside of the scope of this course.
Lecture notes	https://ghislainfourny.github.io/big-data-textbook/
Literature	Papers from scientific conferences and journals. References will be given as part of the course material during the semester.
Prerequisites / Notice	This course is not intended for Computer Science and Data Science students. Computer Science and Data Science students interested in Big Data MUST attend the Master's level Big Data lecture, offered in Fall. Requirements: programming knowledge (Java, C++, Python, PHP, ...) as well as basic knowledge on databases (SQL). If you have already built your own website with a backend SQL database, this is perfect. Attendance is especially recommended to those who attended Information Systems for Engineers last Fall, which introduced the "good old databases of the 1970s" (SQL, tables and cubes). However, this is not a strict requirement, and it is also possible to take the lectures in reverse order.
Competencies	Subject-specific Competencies Concepts and Theories assessed Techniques and Technologies assessed Method-specific Competencies Analytical Competencies assessed Decision-making assessed Media and Digital Technologies fostered Problem-solving assessed Social Competencies Communication assessed Cooperation and Teamwork fostered Sensitivity to Diversity fostered Negotiation assessed Personal Competencies Adaptability and Flexibility fostered Creative Thinking assessed Critical Thinking assessed Integrity and Work Ethics fostered Self-awareness and Self-reflection fostered Self-direction and Self-management fostered

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits	6 credits
Examiners	G. Fourny
Type	session examination
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 180 minutes
Additional information on mode of examination	Working on the exercises is rewarded in the sense of ETH's continuous performance assessment with up to 0.25 bonus points. In principle, it is expected that students solve all exercises. In order to control this, every week, 2 to 3 batches of small assignments (25 in total) will be marked as "bonus point eligible". Students can hand in their solution to these special assignments every week, and they will be graded (pass/fail). Each one of the 25 assignment batches gives 0.01 extra point at the exam, which means that up to 0.25 can be obtained as a bonus. Note that these extra points are added before rounding to a grade with a quarter-point precision and thus may or may not affect the final grade. It is not allowed to register for both the Big Data and the Big Data for Engineers exams, as they mostly contain the same material targeted at difference audiences.
Written aids	General dictionaries are allowed.
Digital exam	The exam takes place on devices provided by ETH Zurich.
Distance examination	It is not possible to take a distance examination.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

Groups

Restrictions

Offered in

Programme	Section	Type
Computational Biology and Bioinformatics Master	Theory	W
DAS in Data Science	Big Data Systems	W
Electrical Engineering and Information Technology Master	Recommended Subjects	W
Electrical Engineering and Information Technology Master	Specialization Courses	W
Computer Science (General Courses)	Computer Science for Non-Computer Scientists	Z
Integrated Building Systems Master	Specialised Courses	W
Physics Bachelor	Electives	W
Computational Science and Engineering Bachelor	Electives	W
Computational Science and Engineering Master	Electives	W
Science, Technology, and Policy Master	Data and Computer Science	W
Statistics Master	Subject Specific Electives	W