Learn about how Hadoop, Spark, and Weka can work collaboratively to produce the best result.
- [Instructor] Tools like Hadoop, Spark, and Weka…can all stand on their own.…Hadoop provides a self-sufficient…distributed system environment…complete with its own distributed file system…called HDFS,…and a default distributed processing solution…such as MapReduce.…Spark can run in a standalone mode…and is not tied to any particular distributed file system.…
You can install Weka on your personal computer…and conduct basic machine learning tasks.…However, at the same time,…they're also all complementary to each other…and can happily coexist…in the same ecosystem,…pulling from each other's strength.…For example, Hadoop is evolving…into an increasingly more open platform.…Starting from version two,…it is using an explicit resource manager called YARN…so that distributed processing engines…other than MapReduce…can be easily plugged into Hadoop…to be able to take advantage…of its Hadoop distributed file system,…or HDFS.…
Because of this development,…we can set up Spark to seamlessly leverage HDFS…as its distributed file system.…
- 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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