Chevron Left
Back to Distributed Computing with Spark SQL

Learner Reviews & Feedback for Distributed Computing with Spark SQL by University of California, Davis

459 ratings
109 reviews

About the Course

This course is all about big data. It’s for students with SQL experience that want to take the next step on their data journey by learning distributed computing using Apache Spark. Students will gain a thorough understanding of this open-source standard for working with large datasets. Students will gain an understanding of the fundamentals of data analysis using SQL on Spark, setting the foundation for how to combine data with advanced analytics at scale and in production environments. The four modules build on one another and by the end of the course you will understand: the Spark architecture, queries within Spark, common ways to optimize Spark SQL, and how to build reliable data pipelines. The first module introduces Spark and the Databricks environment including how Spark distributes computation and Spark SQL. Module 2 covers the core concepts of Spark such as storage vs. compute, caching, partitions, and troubleshooting performance issues via the Spark UI. It also covers new features in Apache Spark 3.x such as Adaptive Query Execution. The third module focuses on Engineering Data Pipelines including connecting to databases, schemas and data types, file formats, and writing reliable data. The final module covers data lakes, data warehouses, and lakehouses. Students build production grade data pipelines by combining Spark with the open-source project Delta Lake. By the end of this course, students will hone their SQL and distributed computing skills to become more adept at advanced analysis and to set the stage for transitioning to more advanced analytics as Data Scientists....

Top reviews


Jun 9, 2020

I highly recommend this course for anyone in the BI and Data space interested in learning Spark. The course gives an easy to understand to the framework and applicable hands on examples.


May 13, 2020

Amazing course that really cuts through the fundamentals of using distributed computing power to analyze and manipulate data. Well organised structure on fundamentals

Filter by:

51 - 75 of 112 Reviews for Distributed Computing with Spark SQL

By Rob E

Jul 6, 2020

Great course. The pedagogy and content were very well done.

By John L

Oct 3, 2020

Very well presented and the exrcises actually work!

By Aaqib A

Aug 30, 2020

Great Course for Beginners...!!!


Mar 21, 2022

Very well Designed and taught.

By Karthik C

Apr 14, 2021

Very Good Course Content

By Phú T

Oct 28, 2021

very easy to follow up!

By Mohammad M R

Apr 17, 2020

Good course for spark

By David M G

May 1, 2020

Fantastic Course!

By pablo s l

Mar 27, 2020

















Nov 18, 2021

cosa bien hecha

By Borysiuk O

May 25, 2020

Nice work! Thnx

By Ammar S

Sep 20, 2021

Great course!

By Estrella P

Jul 21, 2020

Great Course

By Rinrada P

Jul 16, 2020

great course

By Кристијан З

Apr 8, 2022

good course

By Katerine C

Jul 5, 2020

Very good


Apr 19, 2022

good job

By Muhammad I I B J

Jun 25, 2021



May 25, 2020


By Elias A C L

May 15, 2022


By Julio R Y

Nov 16, 2021


By Julio C R F

Nov 4, 2021


By Bryan G G C

Oct 24, 2021


By Erick R M A

Nov 28, 2021


By Justin M

Mar 15, 2021

I liked how the instructors delivered material; their videos were informative and easy to follow, and they did a good job. The assignments were fine, especially the quizzes. I got a good sense of why distributed computing is useful and how to do it in spark.

I will say that the course material might be too complex for the time allotted. I think the instructors did a good job, but some of the topics were pretty advanced and likely required some knowledge that was beyond the scope of the course. Some of the assignments reflected this. In some of them, I was writing most of the code and could follow what was happening pretty easily. By the last assignment, though, I was pulling code from custom packages that the profs had written and doing regressions using equations that they wrote out for me. I think that was necessary-- going through all of this would have been too time-intensive for this course-- but if I had to write a ML model by myself, I doubt I could do it.

BUT, I definitely understand the models on a conceptual level. In this sense, the course was very successful. While I likely couldn't reproduce a lot of the code, I understand the computer science behind a lot of it. So overall, I think the course was very strong.