Learn about the concept and importance of MapReduce.
- HDFS stands for Hadoop Distributed File System.…To conduct distributed processing on HDFS,…we need MapReduce.…MapReduce is a batch processing solution.…Batch processing involves a data set…that doesn't need to be processed immediately…when a transaction occurs.…Let's say that an e-commerce company…stores all its online customer transactions in a database.…
Imagine that an executive wants a weekly sales report.…You can run a batch job every Sunday…to process all the purchase data for that particular week…to produce the necessary information such as market trends.…Since batch jobs don't require real-time processing,…MapReduce has the luxury of spending time…in splitting a big data set…into smaller more manageable chunks.…
It can then move these newly created data sets…across multiple computers…until they are ready to be collapsed into a desired result.…The splitting and shuffling part of MapReduce…is called map tasks…while the collapsing part is referred to as reduce tasks.…To help you understand this concept better,…
Author
Jungwoo Ryoo
Released
8/30/2018- Enabling technologies in data science
- Cloud computing and virtualization
- Installing and working with Proxmox, Hadoop, Spark, and Weka
- Managing virtual machines on Proxmox
- Distributed processing with Spark
- Fundamental applications of machine learning
- Distributed systems and distributed processing
- How Hadoop, Spark, and Weka can work together
Skill Level Beginner
Duration
Views
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Introduction
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Course organization1m 17s
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1. Introduction to Data Science
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Introduction1m 51s
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Data science2m 53s
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Fundamental skills3m 42s
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Enabling technologies2m 4s
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2. Cloud Computing
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Cloud fundamentals3m 29s
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Types of cloud3m 19s
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Solution providers2m 22s
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Proxmox: Installation2m 26s
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3. Distributed File Systems
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Distributed file systems2m 44s
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Fundamentals2m 45s
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Hadoop hands-on2m 8s
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Hadoop: Preparation4m 11s
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Hadoop: Installation4m 18s
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Hadoop: MapReduce hands-on8m 52s
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4. Distributed Processing
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Spark: Installation6m 24s
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Spark: Spark shell4m 28s
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Spark: pyspark4m 32s
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Spark: Application4m 1s
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5. Machine Learning
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Machine learning2m 41s
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Fundamentals2m 16s
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Types of machine learning2m 59s
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Weka: Installation2m 33s
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Weka: GUI3m 35s
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Weka: Training vs. testing3m 21s
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Weka: Clustering2m 12s
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6. Case Study
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Putting it all together2m 42s
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Hadoop cluster: Operation4m 57s
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Spark, YARN, and Hadoop6m 42s
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Weka and Spark3m 12s
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Conclusion
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Next steps41s
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Video: Distributed processing with MapReduce