Course Description

This course on Apache Spark enables learners to utilize Spark for data engineering and machine learning with practical applications. It covers essential topics like Spark Structured Streaming, GraphFrames, ETL for Machine Learning Pipelines, and the implementation of classical machine learning techniques using Spark MLlib.

What Students Will Learn

• Utility of Apache Spark Structured Streaming and its integration with real-time data pipelines.
• GraphFrames integration with Spark and its significance in simplifying graph-based data processing.
• Employing Spark for robust ETL (Extract, Transform, Load) operations tailored for machine learning pipelines.
• Foundational understanding of Spark ML tools for developing machine learning models, including regression and classification techniques.
• Utilizing clustering in Spark ML to derive insights from unlabeled datasets.

Prerequisites or Skills Necessary

Participants should have foundational knowledge in Apache Spark, which can be acquired through introductory courses like IBM's "Big Data, Hadoop and Spark Basics".

Course Content Overview

• Understanding the benefits and applications of Spark Structured Streaming.
• Exploring Graph theory with GraphFrames and its applications.
• Developing effective ETL processes using Apache Spark for machine learning data preparation.
• Applying machine learning techniques within the Spark ecosystem.

Target Audience

This course is designed for data engineers, data scientists, and developers with a foundational understanding of Apache Spark, interested in deepening their knowledge in big data processing and machine learning applications using Spark.

Real-World Application

The skills learned can be applied in designing and implementing scalable data processing pipelines, which are crucial in handling and deriving insights from big data. Understanding Spark's capability in machine learning can lead to more informed, data-driven decisions in a variety of industries.

Syllabus

Module 1: Spark for Data Engineering

• Introduction to Spark Structured Streaming.
• Understanding and applying GraphFrames.
• Workflows in ETL for ML Pipelines.
• Hands-on Lab: ETL for ML Pipelines.

Module 2: Spark ML for Machine Learning

• Core concepts of Spark ML.
• Regression and Classification using Spark ML.
• Clustering techniques.

Module 3: Final Project

• Setup and practice assignment.
• Overview of the project requirements.
• Lab: Final assignment project.
• Project submission and grading.
• Final Quiz.