Learn about the concept and importance of Spark architecture and features.
- Spark brings general purpose computing to Hadoop.…It can handle both real-time and batch processing,…which makes Hadoop more flexible.…In a cluster mode,…which involves multiple computers working together…to get a job done,…you need to designate a Master Computer.…The rest of the computers in the same cluster…are known as Workers.…When you submit a job to the cluster,…one of the Workers starts a Driver Program,…which in turn creates a SparkContext object.…
The SparkContext object then connects to a cluster manager…on the Master Computer,…which could be a Sparks Default Resource Manager or YARN.…Next, Spark acquires Executors…on Worker computers,…which either perform computation or…handle data for the driver.…You can run Spark in a single-node mode too.…Regardless of what mode you are using to run Spark,…you can connect to the Master Computer interactively…and issue commands.…
This interactive environment is called Spark Shell.…Spark is written in Scala.…The Spark Shell gives full access to…all the latest Scala APIs.…
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: Spark architecture and features