Apache Spark for Data Engineering and Machine Learning

Course Overview

What does this course tell?
(Please note that the following overview content is from the original platform)

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 can you get from this course?
We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)
What skills and knowledge will you acquire during this course?
By taking this course, learners will acquire skills and knowledge in Apache Spark Structured Streaming, Graph theory, GraphFrames, ETL, supervised and unsupervised learning, and clustering. They will also gain hands-on experience in applying these skills in labs and a final project.

How does this course contribute to professional growth?
Apache Spark for Data Engineering and Machine Learning is an ideal course for professionals looking to gain hands-on skills to use Spark for data engineering and machine learning applications. The course covers topics such as Spark Structured Streaming, Graph theory, GraphFrames, ETL, supervised and unsupervised learning, and clustering. Through hands-on labs and a final project, learners will gain the skills to use Spark for data engineering and machine learning applications, allowing them to take advantage of the platform's capabilities. This course will help professionals grow their skills and knowledge in the field of data engineering and machine learning, allowing them to stay up-to-date with the latest technologies and trends.

Is this course suitable for preparing further education?
Apache Spark for Data Engineering and Machine Learning is a suitable course for preparing further education. It covers topics such as Spark Structured Streaming, Graph theory, GraphFrames, ETL, supervised and unsupervised learning, and clustering. Learners will also have the opportunity to apply their newly acquired skills in hands-on labs and a final project. Additionally, learners can continue to develop their skills by taking more advanced courses such as "Advanced Apache Spark for Data Science and Machine Learning" or "Apache Spark for Data Science and Machine Learning with Python." Furthermore, learners can explore other related courses such as "Data Science with Python," "Data Science with R," and "Data Science with Scala." Additionally, learners can explore courses related to Big Data such as "Big Data Analysis with Apache Spark" and "Big Data Analysis with Apache Hadoop."