Learn about and be able to explain the concept of Hadoop.
- Hadoop is an innovative open-source software solution…that allows you to create a computer cluster quickly.…The two main components of Hadoop are MapReduce and HDFS.…And they are what makes Hadoop…a distributed computing platform.…HDFS stands for Hadoop Distributed File System.…Beyond this, there are also many tools building…on MapReduce and HDFS to provide additional…and specialized features.…
MapReduce divides up a job to multiple computers…and uses task trackers…to ensure that processing can be done on different servers.…HDFS splits up a big dataset…into smaller, more manageable, file sizes…and stores them on multiple computers.…The main computer is called a master…and the other computers are called slaves.…
Let's look at how Hadoop works.…A typical scenario involves applications posting jobs…to a queue, monitored by a Hadoop master,…which in turn, processes them one-by-one…in the sequence of their arrival.…This type of handling tasks is called batch processing.…Fault tolerance is especially critical…in a distributed computing environment like Hadoop.…
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: Fundamentals