Cloud Infrastructure and Computing

Use of cloud resources for handling large amounts of data

Building and using Big Data applications have various challenges such as load balancing, memory management, scaling, synchronization and asynchronicities . Once the data no longer fits in memory, concepts such as Map / Reduce can be used. This enables the processing of terrabytes of data. Large providers such as Amazon, Microsoft and Google already offer many prefabricated cloud solutions, but these also have their own limitations which are shown in this course.

Big Data The students know the "5 V" of Big Data and understand the problems and challenges that arise in the design, development and operation of Big Data applications.

Data Warehousing Students will be able to select and use sensible storage systems and suitable databases for efficient access to large amounts of data in the terabyte range.

Cloud Provider Students will be able to make a sound decision regarding the use of cloud solutions from major providers such as Amazon, Google and Microsoft. They know the possibilities, long-term costs and limitations of these offerings.

Map / Reduce and Spark Students will understand the Map / Reduce concept and be able to write their own programs that use it and run these programs on a distributed system.

Docker and Kubernetes Students know the benefits and functionality of container virtualization and can run their own applications as containers in a cluster using Kubernetes and Docker.