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CS-310 Lecture 19 - Distributed Computing

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Outline:
0:12 Last time – Computing Platforms
2:00 Easy vs. Hard scalability problems
4:10 “Big Data” problems
5:53 Distributed computing
7:22 Example: Find the largest sales order
9:29 Maximum aggregator is trivially parallelizable
13:35 Basic features of distributed computing
14:16 Maximum aggregator is trivially parallelizable
14:17 Basic features of distributed computing
16:07 Hadoop is a distributed computing platform
19:45 MapReduce
21:29 MapReduce programming model
23:17 MapReduce example: counting words
26:54 Counting words example
29:31 Communication between many nodes
30:32 The Hadoop framework provides:
31:49 Spark improves on MapReduce/Hadoop ideas
33:15 Spark innovations
34:25 Resilient Distributed Datasets (RDDs)
37:09 Spark example: Text Search
39:35 Example: Logistic Regression (ML classification)
42:15 Traditional Parallel Computing
44:23 Recap
45:57 Architectural concepts in this class:
46:14 Class Recap
0:12 Last time – Computing Platforms
2:00 Easy vs. Hard scalability problems
4:10 “Big Data” problems
5:53 Distributed computing
7:22 Example: Find the largest sales order
9:29 Maximum aggregator is trivially parallelizable
13:35 Basic features of distributed computing
14:16 Maximum aggregator is trivially parallelizable
14:17 Basic features of distributed computing
16:07 Hadoop is a distributed computing platform
19:45 MapReduce
21:29 MapReduce programming model
23:17 MapReduce example: counting words
26:54 Counting words example
29:31 Communication between many nodes
30:32 The Hadoop framework provides:
31:49 Spark improves on MapReduce/Hadoop ideas
33:15 Spark innovations
34:25 Resilient Distributed Datasets (RDDs)
37:09 Spark example: Text Search
39:35 Example: Logistic Regression (ML classification)
42:15 Traditional Parallel Computing
44:23 Recap
45:57 Architectural concepts in this class:
46:14 Class Recap
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