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This course presents a holistic approach to Big Data, taking both a top-down and a bottom-up approach to questions such as: What is Big Data? How do we tackle Big Data? Why are we interested in it? What is a Big Data platform?
The course emphasizes that we study Big Data to gain insight that will be used to get people throughout the enterprise to run the business better and to provide better service to customers. Rather than a implementation of a single open-source systems such as Hadoop, the course recommends that Big Data should be processed in a platform that can handle the variety, velocity, and volume of data by using a family of components that require integration and data governance. Big Data is NoHadoop (“not only Hadoop”) as well as NoSQL (“not only SQL”).
Module 1 – Introduction To Graph-Parallel
Question: GraphX extends RDDs, which allows users to use GraphX as a collection, but not as a graph!
Question: Which of the following statements is true?
Question: GraphX unifies Data-Parallelism and Graph-Parallelism in one library.
Module 2 – Visualizing GraphX and Exploring Graph Operators
Question: The “degree” operator returns a VertexRDD[Int] containing the number of outgoing edges of each vertex.
Question: Which of the following is not an attribute of a Triplet class?
Question: Other libraries such as Gephi or GraphLab can help GraphX with visualization.
Module 3 – Modifying GraphX
Question: We must run the “partitionBy” function before running the “groupEdges” operator.
Question: Which of following is among the PartitionStrategies provided by GraphX?
Question: To improve efficiency, GraphX reuses portions of the graph which are unaffected by a modifier.
Module 4 – Neighborhood Aggregation and Caching
Question: aggregateMessages is the only neighborhood aggregation function provided by GraphX.
Question: Which of the following is not an attribute of TripletFields?
Question: The ClassTag is optional for aggregateMessages if the message is a String.
Final Exam
Question: To instantiate a Graph, you need at LEAST 2 RDDs.
Question: pageRank is a graph algorithm that ranks the edges of the graph by correlating their relation with vertices, in terms of both quality and quantity.
Question: The numEdges operator returns an EdgesRDD[Long].
Question: Which of the following ClassTypes are returned from mapTriplets, assuming Graph[VD, ED] is the original?
Question: The reverse operator returns a graph in which the direction of all edges are reversed.
Question: Which of the following ClassTypes are returned from mapTriplets, assuming Graph[VD, ED] is the original?
Question: Caching graphs that are only used infrequently can slow computations.
Question: Which of the following is required to define aggregateMessages?
Question: Triplets are a required parameter when instantiating a Graph.
Question: When defining the merge parameter for groupEdges (Int), which of the following is a valid definition for merge = (Edge1, Edge2)?
Question: In a tuple, the first parameter returned by the “degrees” operator is the degree info, and the second parameter is the vertexid.
Question: Data-Parallel is usually handled by Pregel, GraphLab, and Giraph.
Question: Which of the following is true about GraphX?
Question: By using the mapTriplets function, we are only able to modify the edge attribute.
Question: Which of the following is true about the EdgeContext class?
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