Apache Spark for Data Engineering and Machine Learning

Apache® Spark? is a fast, flexible, and developer-friendly open-source platform for large-scale SQL, batch processing, stream processing, and machine learning. Users can take advantage of its open-source ecosystem, speed, ease of use, and analytic capabilities to work with Big Data in new ways.

In this short course, you explore concepts and gain hands-on skills to use Spark for data engineering and machine learning applications. You'll learn about Spark Structured Streaming, including data sources, output modes, operations. Then, explore how Graph theory works and discover how GraphFrames supports Spark DataFrames and popular algorithms.

Organizations can acquire data from structured and unstructured sources and deliver the data to users in formats they can use. Learn how to use Spark for extract, transform and load (ETL) data. Then, you'll hone your newly acquired skills during your "ETL for Machine Learning Pipelines" lab.

Next, discover why machine learning practitioners prefer Spark. You'll learn how to create pipelines and quickly implement features for extraction, selections, and transformations on structured data sets. Discover how to perform classification and regression using Spark. You'll be able to define and identify both supervised and unsupervised learning. Learn about clustering and how to apply the k-mean s clustering algorithm using Spark MLlib?. You'll reinforce your knowledge with focused, hands-on labs and a final project where you will apply Spark to a real-world inspired problem.

Prior to taking this course, please ensure you have foundational Spark knowledge and skills, for example, by first completing the IBM course titled "Big Data, Hadoop and Spark Basics."

What you'll learn

• Describe the features, benefits, limitations, and application of Apache Spark Structured Streaming
• Describe Graph theory and explain how GraphFrames benefits developers
• Explain how developers can apply extract, transform and load (ETL) processes using Spark.
• Describe how Spark ML supports machine learning development
• Apply Spark ML for regression and classification
• Differentiate between supervised and unsupervised Machine learning"
• Explain how Spark ML uses clustering
• Demonstrate hands-on working knowledge of using Spark for ETL processes