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Distributed Computing with Spark SQL

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 reading/writing data, and 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.

Created by University of California, Davis


What you’ll learn

Through this learning resource you will improve the skills sought by employers in 2021. The most in demand technique within the learning resource that is often requested from companies is Data Analysis. The most in demand tool is SQL. You will also learn about Collaboration Skills, a trait frequently requested in job maps.

Who will benefit?

Comparing the description from this learning opportunity with nearly 10,000 data-related job maps, we determine that those interested in Data Scientist roles would benefit the most.