Distributed Computing with Spark SQL


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Description

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.

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is about SQL

SQL (S-Q-L, "sequel"; Structured Query Language) is a domain-specific language used in programming an...

supervised by University of California, Davis

UC Davis, one of the nation’s top-ranked research universities, is a global leader in agriculture, ve...

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