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Big Data Fundamentals with PySpark
4.7(224)65k studentsADVANCEDEnglish
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Course Overview
This course introduces large-scale data processing using Apache Spark and PySpark. Learners explore memory-accelerated cluster computing and leverage high-level libraries like SparkSQL and MLlib for data analysis and machine learning.
Who Should Take This Course
Intended for advanced data engineers and data scientists preparing to handle massive datasets efficiently using Python and Spark environments.
Key Takeaways
- Understand the core architecture of Apache Spark and how PySpark outperforms traditional Hadoop jobs.
- Query structured data with SparkSQL and build machine learning workflows using MLlib.
- Apply clustering and analysis techniques to real-world textual, sports, and genomic data.
About this course
There's been a lot of buzz about Big Data over the past few years, and it's finally become mainstream for many companies. But what is this Big Data? This course covers the fundamentals of Big Data via PySpark. Spark is a "lightning fast cluster computing" framework for Big Data. It provides a general data processing platform engine and lets you run programs up to 100x faster in memory, or 10x faster on disk, than Hadoop. You’ll use PySpark, a Python package for Spark programming and its powerful, higher-level libraries such as SparkSQL, MLlib (for machine learning), etc. You will explore the works of William Shakespeare, analyze Fifa 2018 data and perform clustering on genomic datasets. At the end of this course, you will have gained an in-depth understanding of PySpark and its application to general Big Data analysis.
Price
Free
This course is free to enrol.
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