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,…
- 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
Course organization1m 17s
1. Introduction to Data Science
2. Cloud Computing
3. Distributed File Systems
4. Distributed Processing
5. Machine Learning
6. Case Study
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