Learn about the concept and importance of Spark as a real-time distributed processing engine and its different modes of operation.
- Unlike MapReduce, Spark is capable…of stream processing.…Stream processing refers to realtime handling of data.…It's ideal when you need instant feedback…from a data analytics tool.…Let's say that you are developing an anomaly detection tool.…In this scenario, you cannot afford to wait…until the end of the week when your batch job…is supposed to run.…You need to respond to an anomaly immediately.…
What makes Spark fast is it's in-memory processing.…All the processing is done in the main memory,…which is much faster than storage devices,…but the drawback is the cost.…The memory chips are more expensive than the hard drives.…MapReduce is a default distributed processing…solution for Hadoop, but Hadoop allows you…to use Spark instead.…In fact, there are multiple ways you can…use Spark with Hadoop.…
The first option is using Spark in a standalone mode.…In this mode, you can run Spark alongside…an existing Hadoop installation.…To access HTFS from Spark, you just need an HTFS URL.…The second option is using YARN,…which was introduced from Hadoop 2.0.…
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 Spark