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In this learning path, you will:
In Partnership with Dr. Majd Sakr and Carnegie Mellon University.
Get an introduction to virtualization, one of the many technologies that power the cloud. Learn what virtualization is, its benefits, and how cloud resources are shared.
Take a deep dive into virtualization by understanding its formal definition as well as the different types of virtual machines.
Learn about how computing power is virtualized to create cloud resources. Covers different types of CPU virtualization.
Learn how memory is virtualized to support cloud resources. Covers different types of memory mapping and related issues.
Learn about how computer input/output is virtualized. Covers I/O basics and a case study of virtualizing I/O.
Learn about how storage and network resources can be virtualized to support software defined datacenters.
Discover what the cloud is, the history of cloud computing, what types of clouds are available, and how you might use the cloud.
Learn how developers pay to use the cloud and how cloud providers are able to charge for their services. Get an overview of how the cloud is kept secure.
Datacenters power the cloud. Learn what they are, how they work, and challenges in managing them.
Take a deep dive into datacenters to discover how they are laid out and how computing resources are provisioned and metered.
Get into the nitty-gritty of cloud computing to understand how resources are provisioned, billed, and monitored. Learn about how to orchestrate cloud infrastructure with examples.
Learn how developers write programs that run on the cloud, including how to deploy, be fault-tolerant, load balance, scale, and deal with latency.
Learn all about data and get an overview of how it's stored, including local and distributed file systems, databases, and object storage.
Discover how distributed file systems work, then learn about Hadoop and Ceph.
Learn about two more types of storage - NoSQL databases and object storage - with case studies from industry.
Learn about distributed programming and why it's useful for the cloud, including programming models, types of parallelism, and symmetrical vs. asymmetrical architecture.
MapReduce was a breakthrough in big data processing that has become mainstream and been improved upon significantly. Learn about how MapReduce works.
GraphLab is a big data tool developed by Carnegie Mellon University to help with data mining. Learn about how GraphLab works and why it's useful.
Spark is an open-source cluster-computing framework with different strengths than MapReduce has. Learn about how Spark works.
The increase of available data has led to the rise of continuous streams of real-time data to process. Learn about different systems and techniques for consuming and processing real-time data streams.