Learn about the concept and significance of cloud computing and virtualization in data science.
- Cloud computing refers to the use…of a collection of remote servers,…connected via computer networks…and available through the internet.…Virtualization is what implements cloud computing.…It blurs the boundaries between physical computers…by allowing multiple logical machines to be created…on one hardware platform, like a server.…These two things in conjunction play such an important role…in making data science applications possible.…
Let's explore why.…A large number of organizations today…still use their local servers…but many of them have already migrated…to the cloud computing model due…to its more economically-efficient nature.…Why is cloud computing cheaper?…Well, that's partially due to economies of scale.…Basically, what that means is…that more volume leads to more savings.…You see, cloud computing companies specialize…in managing server farms.…
That means they're handling a lot of volume.…They're distributing their fixed costs over more services.…They have their know-how in maximizing their profit…while minimizing their expenses.…
Author
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: Cloud computing and virtualization