Back to Distributed Computing with Spark SQL
Learner Reviews & Feedback for Distributed Computing with Spark SQL by University of California, Davis
About the Course
This course is for students with SQL experience and now want to take the next step in gaining familiarity with distributed computing using Spark. Students will gain an understanding of when to use Spark and how Spark as an engine uniquely combines Data and AI technologies at scale.
The four modules build on one another and by the end of the course the student will understand:
• Spark Architecture
• Spark DataFrame
• Optimizing the reading/writing of data
• How to build a machine learning model
The first module will introduce Spark, including how Spark works with distributed computing and what are Spark Dataframes. Module 2 covers the core concepts of Spark such as storage vs. computing, caching, partitions and Spark UI. The third module looks at Engineering Data Pipelines covering connecting to databases, schemas and type, file formats and writing good data. The final module looks at the application of Spark with Machine Learning through the business use case, a short introduction to what machine learning is, building and applying models and a final course conclusion.
By understanding when to use Spark, either scaling out when the model or data is too large to process on a single machine, or having a need to simply speed up to get faster results, students will hone their SQL skills and become a more adept Data Scientist.
Filter by:
1 - 1 of 1 Reviews for Distributed Computing with Spark SQL
By Sizhe L
•Oct 26, 2019
video quality needs to be improved. Be careful about the last assignment. The accuracy asked in the question is the accuracy over the training data.
Coursera provides universal access to the world’s best education,
partnering with top universities and organizations to offer courses online.
© 2019 Coursera Inc. All rights reserved.
