Big Data

Official Moodle

Communication with the students, including sharing the material, exercises and quizzes, occurs on the ETH Moodle system.

Some of the material (slides, recordings) is also available publicly on this page, by scrolling down.

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. 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.

Lecture recordings

The recordings are publicly available on external page YouTube.