Distributed computing on the cloud: GraphLab
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.
Learning objectives
In this module, you will:
- Describe the unique features in GraphLab and the application types that it targets
- Recall the features of a graph-parallel distributed programming framework
- Recall the three main parts in the GraphLab engine
- Describe the steps that are involved in the GraphLab execution engine
- Discuss the architectural model of GraphLab
- Recall the scheduling strategy of GraphLab
- Describe the programming model of GraphLab
- List and explain the consistency levels in GraphLab
- Describe the in-memory data placement strategy in GraphLab and its performance implications for certain types of graphs
- Discuss the computational model of GraphLab
- Discuss the fault-tolerance mechanisms in GraphLab
- Identify the steps that are involved in the execution of a GraphLab program
- Compare and contrast MapReduce, Spark, and GraphLab in terms of their programming, computation, parallelism, architectural, and scheduling models
- Identify a suitable analytics engine given an application's characteristics
In partnership with Dr. Majd Sakr and Carnegie Mellon University.
Prerequisites
- Understand what cloud computing is, including cloud service models and common cloud providers
- Know the technologies that enable cloud computing
- Understand how cloud service providers pay for and bill for the cloud
- Know what datacenters are and why they exist
- Know how datacenters are set up, powered, and provisioned
- Understand how cloud resources are provisioned and metered
- Be familiar with the concept of virtualization
- Know the different types of virtualization
- Understand CPU virtualization
- Understand memory virtualization
- Understand I/O virtualization
- Know about the different types of data and how they're stored
- Be familiar with distributed file systems and how they work
- Be familiar with NoSQL databases and object storage, and how they work
- Know what distributed programming is and why it's useful for the cloud
- Understand MapReduce and how it enables big data computing
- Understand Spark and how it differs from MapReduce