Ghislain Fourny: Katalogdaten im Herbstsemester 2017

Auszeichnung: Die Goldene Eule
NameHerr Dr. Ghislain Fourny
Adresse
Dep. Informatik
ETH Zürich, STF H 311
Stampfenbachstrasse 114
8092 Zürich
SWITZERLAND
Telefon+41 44 632 31 55
E-Mailgfourny@inf.ethz.ch
URLhttp://people.inf.ethz.ch/gfourny
DepartementInformatik
BeziehungDozent

NummerTitelECTSUmfangDozierende
252-0834-00LInformation Systems for Engineers Information 4 KP2V + 1UG. Fourny
KurzbeschreibungThis course provides the basics of information systems from the perspective of the user. The main focus is on relational databases, including tabular data, the relational algebra, the SQL query language, schema design, normal forms, physical architecture, indices. The course also covers support for data cubes on top of relational databases.
LernzielAfter visiting this course, students should be capable to:

1. Explain, in the big picture, how a relational database works and what it can do in their own words.
2. Explain the relational data model (tables, rows, attributes, primary keys, foreign keys), formally and informally, including the relational algebra operators (select, project, rename, all kinds of joins, division, cartesian product, union, intersection, etc).
3. Perform non-trivial reading SQL queries on existing relational databases, as well as insert new data, update and delete existing data.
4. Design a new relational schema to store data in accordance to the real world's constraints, such as relationship cardinality.
5. Adapt and improve an existing schema to make it more robust against anomalies, thanks to a very good theoretical knowledge of normal forms.
6. Understand how indices work (hash indices, B-trees), how they are implemented, and how to use them to make queries faster.
7. Access an existing relational database from a host language such as Java, using bridges such as JDBC.
8. Explain data independence.
9. Explain how a relational database is physically implemented.
10. Know and deal with the natural syntax for relational data, CSV.
11. Explain the data cube model including slicing and dicing.
12. Store data cubes in a relational database.
13. Map cube queries to SQL.
14. Slice and dice cubes in a UI.
InhaltUsing a relational database
=================
1. Introduction (historical overview, data independence, data shapes)
2. The relational model (data models, tables, relational algebra, CSV syntax)
3. The query language SQL (DML 1 of 2, SQL shell, counterpart of selection, projection, grouping, ordering, renaming)
4. Schema definitions (DDL, data types, SQL)
5. Updates (DML 2 of 2, insertion, deletion, updates, SQL)

Taking a relational database to the next level
=================
6. Best practices and normal forms (update/insert/delete anomalies, first, second, third, BC, fourth)
7. Physical architecture of a relational database (storage levels, tuple storage)
8. Indices and optimization (ISAM, B-tree, B+-tree, hash)
9. Communicating with a SQL database from a host language (Java, JDBC)

Analytics on top of a relational database
=================
10. Analytics, data warehousing, OLAP vs. OLTP, the data cube model (dimensions, algebra)
11. Storing and querying data cubes in a relational database (star schema, snowflake schema)
12. Data cube optimization (indices, bitmaps)

Outlook
=================
13. Limits of tables and cubes (decision points for when and when not to use them vs. trees, graphs)
14. Limits of scaling up a single machine and transition to Big Data (introduction to data denormalization on simple cases, "hacking" a relational database onto several machines and issues)
Literatur- Lecture material (slides).

- Book: "Database Systems: The Complete Book", H. Garcia-Molina, J.D. Ullman, J. Widom
(It is not required to buy the book, as the library has it)
Voraussetzungen / BesonderesFor non-CS/DS students only, BSc and MSc
Elementary knowledge of set theory and logics
Knowledge as well as basic experience with a programming language such as Pascal, C, C++, Java, Haskell, Python
263-3010-00LBig Data Information Belegung eingeschränkt - Details anzeigen 8 KP3V + 2U + 2AG. Fourny
KurzbeschreibungThe 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.
LernzielThis 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.
InhaltThis 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: 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.

Guest lectures planned so far:
- Bart Samwel (Google) on F1, Spanner
LiteraturPapers from scientific conferences and journals. References will be given as part of the course material during the semester.
Voraussetzungen / BesonderesThis course, in the autumn semester, is only intended for:
- Computer Science students
- Data Science students
- CBB students with a Computer Science background

Another version of this course will be offered in Spring for students of other departments. However, if you would like to already start learning about databases now, a course worth taking as a preparation/good prequel to the Spring edition of Big Data is the "Information Systems for Engineers" course, offered this Fall for other departments as well, and introducing relational databases and SQL.