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What you'll learn

Explore the fundamental concepts of Big Data and the components of the Hadoop ecosystem.

Explain the architecture and key principles of Apache Spark and its role in big data processing.

Utilize RDD transformations and actions to effectively process large-scale datasets with PySpark.

Execute advanced DataFrame operations, including data manipulation and aggregation techniques.

Skills you'll gain

Apache Spark

Data Transformation

Pandas (Python Package)

Big Data

Data Processing

PySpark

Data Manipulation

SQL

Apache Hadoop

There are 5 modules in this course

By the end of this course, you will be able to: - Explore foundational concepts of Big Data and the components of the Hadoop ecosystem - Explain the architecture and key principles underlying Apache Spark - Utilize RDD transformations and actions to process large-scale datasets with PySpark - Execute advanced DataFrame operations, including handling complex data types and performing aggregations - Evaluate and enhance data processing workflows by leveraging PySpark SQL and advanced DataFrame techniques This course is ideal for learners who are new to data engineering and want to understand how to use PySpark effectively. Basic knowledge in Python is recommended, but no prior experience with PySpark is necessary. Start your journey with PySpark and build a strong foundation in distributed data processing!

Working with RDD

PySpark DataFrames

PySpark SQL

Course Wrap Up and Assessment