COURSE OBJECTIVE:
By the end of this course, you'll not only have understood how to use machine learning extensions and structured streams but you'll also be able to apply Spark in your own upcoming big data projects.
After completing this course, you will be able to:
• Write your own Python programs that can interact with Spark
• Implement data stream consumption using Apache Spark
• Recognize common operations in Spark to process known data streams
• Integrate Spark streaming with Amazon Web Services
• Create a collaborative filtering model with Python and the movielens dataset
• Apply processed data streams to Spark machine learning APIs
TARGET AUDIENCE:
This course is aimed at IT professionals seeking to learn Spark to process big data. This course is get you up and running with Apache Spark and Python. You'll integrate Spark with AWS for real-time analytics. Finally, you'll apply processed data streams to machine learning APIs of Apache Spark.
Big Data Processing with Apache Spark is for you if you are a software engineer, architect, or IT professional who wants to explore distributed systems and big data analytics.
COURSE PREREQUISITES:
No prior knowledge of Spark is required, however previous experience of working with Python is recommended.
COURSE CONTENT:
Lesson 1: Introduction to Spark Distributed Processing
• Introduction to Spark and Resilient Distributed Datasetsll
• Operations Supported by the RDD API
• Self-Contained Python Spark Programs
• Introduction to SQL, Datasets, and DataFrames
Lesson 2: Introduction to Spark Streaming
• Streaming Architectures
• Introduction to Discretized Streams
• Windowing Operations
• Introduction to Structured Streaming
Lesson 3: Spark Streaming Integration with AWS
• Spark Integration with AWS Services
• Integrating AWS Kinesis and Python
• AWS S3 Basic Functionality
Lesson 4: Spark Streaming, ML, and Windowing Operations
• Spark Integration with Machine Learning
FOLLOW ON COURSES:
Not available. Please contact.