Spark Streaming - Stream Processing in Lakehouse - PySpark

Learn Real-time Stream Processing with Apache Spark and Databricks! This practical course guides you through building robust stream processing solutions using Python and the PySpark API.

What you'll discover:

• Real-time Stream Processing Fundamentals: Understand the core concepts of real-time data processing with Apache Spark and Databricks Cloud.
• Hands-on with PySpark: Master Python programming for efficient data manipulation and stream processing using the PySpark API.
• Practical Implementation: Follow a working session-like approach with live coding examples to solidify your understanding. We'll explain every concept step-by-step.
• End-to-End Capstone Project: Apply your skills to a real-world project, covering design, coding, implementation, testing, and CI/CD (Continuous Integration/Continuous Deployment) practices.

Who should take this course?

This course is designed for:

• Software Engineers: Looking to develop Real-time Stream Processing Pipelines and applications with Apache Spark.
• Data Engineers & Data Architects: Responsible for designing and building data-centric infrastructure.
• Managers & Architects: Who work with teams implementing Apache Spark and need a solid understanding of the process.

Technology Stack:

This course utilizes Apache Spark 3.5 and is tested on Azure Databricks Cloud using Databricks Runtime 14.1. You'll gain experience with the latest technologies in the Lakehouse architecture.