UDUdemy
Taming Big Data with Apache Spark and Python
Intermediate 7 hours English Completion Certificate Certificate
About this course
This course covers Apache Spark with PySpark from distributed computing fundamentals through production patterns: resilient distributed datasets, the DataFrame API, Spark SQL for analytical queries, MLlib for distributed machine learning, and Spark Streaming for real-time data processing.
Students run Spark on local clusters and connect to AWS EMR for cloud-scale processing. The curriculum addresses the most common Spark performance issues: partition tuning, join strategies, and avoiding data skew.
What you'll learn
Process large datasets with PySpark's RDD and DataFrame APIs
Write Spark SQL queries for analytical processing at scale
Build distributed ML pipelines with Spark MLlib
Process streaming data with Spark Structured Streaming
Tune Spark jobs for performance with partition and memory optimization
This course includes
7h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
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Instructor
FK
Frank Kane
Udemy instructor
200K+ learners12 courses4.5 instructor rating
Taught by Frank Kane, former Amazon engineer with extensive distributed systems experience who specializes in practical big data education.
Requirements
- Python proficiency; basic SQL knowledge; some data analysis experience
Who this course is for
- Data engineers building large-scale data processing pipelines
- Data scientists who need to process datasets too large for pandas
- Python developers entering big data or data engineering roles
About this provider
UD
Udemy
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Frequently asked questions
Yes — Spark runs on Databricks, EMR, and Azure Synapse and is the dominant big data processing framework in enterprise data engineering.
No — Spark runs locally on a single machine for learning, and AWS free tier covers cloud practice.