PySpark Development Course Overview

Target Audience for PySpark Development

PySpark Development is a course designed to educate professionals on distributed data processing using Apache Spark with Python.

• Data Engineers
• Data Scientists
• Big Data Analysts
• Software Engineers involved in data processing
• Machine Learning Engineers
• IT Professionals seeking to understand big data technology stack
• Analytics Professionals
• Research Scientists
• Technical Architects
• Developers transitioning from other data processing frameworks

Learning Objectives - What you will Learn in this PySpark Development?

Introduction to Learning Outcomes

In this PySpark Development course, students will gain practical skills in data processing with PySpark, understanding RDDs, DataFrames, and performance optimization techniques.

Learning Objectives and Outcomes

• Comprehend the fundamentals of PySpark and Apache Spark's ecosystem, including its architecture and execution model.
• Learn to create and manipulate Resilient Distributed Datasets (RDDs), and understand their role in distributed data processing.
• Grasp the concept of lazy execution and how transformations and actions trigger computation in Spark.
• Master the use of RDD transformations such as map, filter, flatMap, distinct, sample, join, and repartition to process large datasets.
• Execute actions on RDDs like collect, reduce, count, foreach, aggregate, and save to extract results and perform aggregations.
• Develop the ability to create, interact with, and manipulate Spark DataFrames, leveraging their schema and SQL-like capabilities.
• Implement complex data transformations and understand how to join multiple DataFrames for comprehensive data analysis.
• Apply statistical transformations and aggregate functions to analyze and summarize large datasets.
• Recognize the efficient use of Spark SQL for querying data and the advantages of temporary tables for session-based data exploration.
• Identify the pitfalls of User-Defined Functions (UDFs) and learn best practices in data partitioning and serialization to optimize Spark application performance.