Apache Spark with Python - Big Data with PySpark and Spark

In this 3-hour course, learn how to harness the power of Apache Spark with Python using PySpark. Through practical, hands-on examples, you'll master big data processing and analytics techniques, enabling you to craft scalable applications and gain insights from diverse datasets.

What I will be able to do after this course

• Understand the architecture of Apache Spark and its components.
• Learn to work with Resilient Distributed Datasets (RDDs) and perform transformations and actions.
• Develop skills in processing structured and semi-structured data using Spark SQL and DataFrames.
• Explore optimization techniques such as caching and partitioning to enhance Spark application performance.
• Gain experience deploying and scaling Spark applications on platforms like Hadoop YARN and Amazon EMR.

Course Instructor(s)

Your instructor, James Lee, is a seasoned software engineer with over a decade of experience in big data technologies. He specializes in simplifying complex subjects by creating engaging and accessible video tutorials. With a practical approach to teaching, James equips students with the skills and confidence to tackle real-world data challenges.

Who is it for?

This course is perfect for software engineers looking to develop their expertise in big data technologies, as well as data scientists and engineers aiming to enhance their data processing proficiency. It's suitable for learners with basic programming experience who aspire to work with Apache Spark and gain hands-on experience building data-driven solutions. Aspiring big data professionals will find the content insightful and career-enriching.