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Course Outline
Introduction
- Overview of Spark and Hadoop features and architecture
- Understanding big data
- Python programming basics
Getting Started
- Setting up Python, Spark, and Hadoop
- Understanding data structures in Python
- Understanding PySpark API
- Understanding HDFS and MapReduce
Integrating Spark and Hadoop with Python
- Implementing Spark RDD in Python
- Processing data using MapReduce
- Creating distributed datasets in HDFS
Machine Learning with Spark MLlib
Processing Big Data with Spark Streaming
Working with Recommender Systems
Working with Kafka, Sqoop, Kafka, and Flume
Apache Mahout with Spark and Hadoop
Troubleshooting
Summary and Next Steps
Requirements
- Experience with Spark and Hadoop
- Python programming experience
Audience
- Data scientists
- Developers
Testimonials
- The trainer is open to questions, and the training is in interactive way. I like this point. - The trainer was able to efficiently manage the participation of remote persons who weren't able to be present in the office.
Arnaud CAPITAINE, Adikteev
It was interesting, got the chance to learn more about machine learning and Spark stack of technologies.
Edina Kiss, Accenture Industrial SS
The fact that we were able to take with us most of the information/course/presentation/exercises done, so that we can look over them and perhaps redo what we didint understand first time or improve what we already did.
Raul Mihail Rat - Edina Kiss, Accenture Industrial SS
I liked that it managed to lay the foundations of the topic and go to some quite advanced exercises. Also provided easy ways to write/test the code.
Ionut Goga - Edina Kiss, Accenture Industrial SS
The live examples
Ahmet Bolat - Edina Kiss, Accenture Industrial SS
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