263-3010-00L Big Data
| Semester | Herbstsemester 2024 |
| Dozierende | G. Fourny |
| Periodizität | jährlich wiederkehrende Veranstaltung |
| Lehrsprache | Englisch |
Lehrveranstaltungen
| Nummer | Titel | Umfang | Dozierende | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 263-3010-00 V | Big Data | 3 Std. |
| G. Fourny | ||||||
| 263-3010-00 U | Big Data Gruppeneinteilung erfolgt über myStudies. | 2 Std. | G. Fourny | |||||||
| 263-3010-00 A | Big Data Individual work to get hands-on experience with the technologies covered, no fixed presence required. | 4 Std. | G. Fourny |
Katalogdaten
| Kurzbeschreibung | 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. | |||||||||||||||||||||||||||||||||
| Lernziel | Do you want to be able to query your own data productively and efficiently in your future semester projects, master thesis, or PhD thesis? Are you looking for something beyond the Python+Pandas hype? This courses teaches you how to do so as well as the dos and don'ts. "Big Data" refers to the case when the amount of data is very large (100 GB and more), or when the data is not completely structured (or messy). The Big Data 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 are 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 one of your projects efficiently and consistently. | |||||||||||||||||||||||||||||||||
| Inhalt | 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. 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 third normal form. - physical storage: distributed file systems (HDFS), object storage(S3), key-value stores - logical storage: document stores (MongoDB), column stores (HBase), graph databases (neo4j), data warehouses (ROLAP) - data formats and syntaxes (XML, JSON, RDF, Turtle, CSV, XBRL, YAML, protocol buffers, Avro) - data shapes and models (tables, trees, graphs, cubes) - 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, XQuery, JSONiq, Cypher, MDX) - 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, neo4j) - 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. | |||||||||||||||||||||||||||||||||
| Literatur | Course textbook: https://ghislainfourny.github.io/big-data-textbook/ Papers from scientific conferences and journals. References will be given as part of the course material during the semester. | |||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | The lecture is hybrid, meaning you can attend with us in the lecture hall, or on Zoom, or watch the recordings on YouTube later. Exercise sessions are in presence. This course, in the autumn semester, is only intended for: - Computer Science students - Data Science students - CBB students with a Computer Science background Mobility students in CS are also welcome and encouraged to attend. If you experience any issue while registering, please contact the study administration and you will be gladly added. For students of all other departements interested in this fascinating topic: I would love to have you visit my lectures as well! So there is a series of two courses specially designed for you: - "Information Systems for Engineers" (SQL, relational databases): this Fall - "Big Data for Engineers" (similar to Big Data, but adapted for non Computer Scientists): Spring 2023 There is no hard dependency, so you can either them in any order, but it may be more enjoyable to start with Information Systems for Engineers. Students who successfully completed Big Data for Engineers are not allowed to enrol in the course Big Data. | |||||||||||||||||||||||||||||||||
Kompetenzen |
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Leistungskontrolle
| Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird) | |
Leistungskontrolle als Semesterkurs | |
| ECTS Kreditpunkte | 10 KP |
| Prüfende | G. Fourny |
| Form | Sessionsprüfung |
| Prüfungssprache | Englisch |
| Repetition | Die Leistungskontrolle wird nur in der Session nach der Lerneinheit angeboten. Die Repetition ist nur nach erneuter Belegung möglich. |
| Prüfungsmodus | schriftlich 180 Minuten |
| Zusatzinformation zum Prüfungsmodus | 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/truncating 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. |
| Hilfsmittel schriftlich | General dictionaries are allowed. This includes general English dictionaries (with word definitions) as well as general bilingual dictionaries (English <-> other language). Specialized dictionaries are not allowed. Dictionaries cannot be annotated by hand. |
| Digitale Prüfung | Die Prüfung findet auf Geräten statt, die von der ETH Zürich zur Verfügung gestellt werden. |
| Fernprüfung | Das Ablegen als Fernprüfung ist nicht möglich. |
| Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan. | |
Lernmaterialien
Gruppen
| 263-3010-00 U | Big Data | ||||||
| Gruppen | G-01 |
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Einschränkungen
| Vorrang | Die Belegung der Lerneinheit ist nur durch die primäre Zielgruppe möglich |
| Primäre Zielgruppe | Cyber Security MSc (260000)
Cyber Security MSc (EPFL) (260100) Data Science MSc (261000) Computational Biology and Bioinformatics MSc (262200) Computational Biology and Bioinformatics MSc (262200) Informatik MSc (263000) Doktorat Informatik (264002) DAS ETH in Data Science (266000) CAS ETH in Informatik (269000) Informatik (Mobilität) (274000) |