MapReduce in 10 Minutes
summary
Explains that MapReduce splits a huge input into chunks, maps items into key/value pairs (example: letters mapped as A: 2, A:1, A:0 → A: 3; B: 6,0,8 → B: 14), shuffles by key, then reduces to totals; shows word-count on Internet Archive producing counts like bear: 2, car: 3, river: 2. Recommends Apache Hadoop implementation and links to Hadoop docs, cognitiveclass.ai and a Talend primer.
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chapters (8)
- 0:00Intro
- 0:30Functional Programming
- 1:14MapReduce
- 3:34Summarize
- 4:47Distributed Compute
- 5:24Distributed File System
- 8:02Word Count Processing
- 9:20MapReduce Implementation
