Excellent overview of Spark, including exercises that solidify what you learn during the lectures. The development environment setup tutorials were also very helpful, as I had not yet worked with sbt.
The sessions where clearly explained and focused. Some of the exercises contained slightly confusing hints and information, but I'm sure those mistakes will be ironed out in future iterations. Thanks!
by Huajian M•
by IURII B•
by Estera K•
by Satendra k•
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by Kiệt Đ•
by Bianca T•
Taking into consideration that this was the first edition of the course, I can say that it has been a nice journey. I am glad about the fact that Heather managed to expose a bit of the Spark internals and not only talk about querying data and how easily this can be made by using Spark (as most of the Spark oriented courses consist of).
In addition to this, I could listen to Heather all day long - she's a great presenter and has wonderful teaching skills.
However, the homework has outlined some neglected aspects of the course:
- vague description or requirements
- not strongly related to the presented content (the lectures outlined partitioning mechanism, but the homework 2 did not require it...)
- not so meaningful feedback, except for some tests failing/passing - I would have expected something like you did ok, but your job took longer than expected; check out this and that
Overall, it's been a highly expected course and it was nice to get a broader outlook on Spark. I hope that there will be more courses (and more detailed) related to Spark ecosystem in the near future.
by Anton M•
Really enjoyed most part of the course, it was a fun ride with Spark !
Explanations of lector was crystal clear and I liked all assignments (except last one)
There are some cons though:
-> Week 3 contains no assignment, I would prefer to have one really dedicated to "Partition and Shuffling" subject
-> Spark SQL explanations about untyped were too much shady. It somehow feels like this API goes totally orthogonal to everything functional we have had so far. It's like running in Java but using C with JNI... Well, after all, it's a drawback of API, not course itself, but still having bit of aftertaste of fighting with Scala type system trying to glue SQL... meh
-> there are many missed opportunities to have proper Coursera quiz during lectures
by Robin B•
Very good introduction to RDDs and DataFrames/Dataset along with valuable insight into performance considerations.
I'd done some prior work with Hadoop/Pig in the past and more recently with Spark (mainly DataFrames/GraphFrames) - this was really useful to round out my understanding of RDDs and optimisation.
The assignment guidance in the code comments could be more complete to save having to refer back to the site (and maybe reference specific video lectures with the hints). Though it's good that the assignment exercises aren't tutorial-grade, as that makes the experience more transferable to real projects.
by Saurabh M•
Dr Heather has done an outstanding job to create this unique material with a fine combination of theoretical and practical aspect of Spark. She has covered almost everything from basic to complex, but there is some area which demands more time from the creator for its explanations. Since this is first time launched course and definitely going to improve itself in upcoming days. I enjoyed this course thoroughly. It helped me, cementing my basic concept of Spark.
by Ellen K•
The structure, focus content of the videos of this course are great. The assignments are so-so. They do practice writing reasonably realistic Spark jobs in Scala, but it is hard to draw the connection between the more theoretical videos and the very practical assignments. Also, the assignments are hard to solve due to being poorly specified and there being hardly any helpful output from the auto-graders used to evaluate assignment submissions.
by Nikita P•
The video lectures are good but code assignments are worse, seems like they were written by students instead of professor or something. Sometimes code doesn't follow Scala and FP conventions. And the output of the grader doesn't really provide any helpful information besides the name of the faulty function. But overall it's a good course and I think the newcomers without any previous experience with Spark will learn a lot.
by Chet W•
Great lectures but the exercises felt contrived sometimes. Especially the exercise on PCA didn't really seem to provide that much insight into the data or illustrate the usefulness of the algorithm (especially when compared to the parallel programming exercise which had a great use for PCA). However the last exercise was good and forced the student to really explore the spark API. Learned so much from this!
by Kiarash A•
The teaching was amazing, the concepts were also. But about the assignments, well they were very informative for me, but for some of the problems I think I couldn't find the road without reading the discussion forums... Maybe there is a need to update some of assignments and add some more information or test cases to save learners time.
Anyway... that was cool, thanks for your effort Coursera.
by Sergio L•
I thought this was a decent course. I enjoyed the exercises and thought it gave a good introduction to Spark. Some of the lectures in Week4 were a bit long and the material needed to complete the Week4 exercise wasn't in the lectures. It would have been nice to have a 'conclusion' lecture wrapping everything up instead of just ending the course on a DataSet's lecture.
by Philip R K•
Generally a really good introduction to Spark. What I found disturbing though were the very imbalanced difficulties of the excercises and the rather uninformative test messages that did not help for the implementation. There was no course where I had to search for other peoples suggestions in the forums.
Still the course was good -- I would do it again!
by Simon M•
Gives a really solid overview of the foundations of the Spark programming model, where it came from, and how latency affects this model for a distributed cluster. Explains well the key differenced between RDDs, Datasets, and Dataframes. Thought the videos were unnecessarily long and could do with "sharpening" them up a bit.
by Isaac A•
This course helped me understand Scala and Spark main operations as well as their use cases. Moreover, exercise directions for Week 1, 2 and 4 should be more clear. To all students, you don't need to install Hadoop to complete this course. I recommend you to use IntelliJ over Eclipse.
by Viacheslav I•
Very good course! Practical and industry-useful. Would be great only if there were a bit more programming assignments, with more fine grained structure, so that one could practice more in simple things, not only trying to fill out ??? marks left alone. Overall happy that participated!
by Nag K•
Course Assignments consumed more time than anticipated, as they required the knowledge from upcoming week's video lectures. Had it not been for someone mentioning about this in discussion forums, it would've consumed more time for me to complete the assignments
by Ashish M•
A nice course to start with learning basics of Spark with Scala, however it has missing things like broadcast variables, what are tasks/executors in Spark etc. The course is mainly around how to do distributed execution using scala via Spark, not the vice-versa.
by William S•
On a scale of 1 to 10 with 10 being the most familiar with Scala that you can be, it is very helpful to be at least a 6 or 7 for this course to code everything efficiently. Concepts covered here are very helpful though and it is a useful introduction to Spark.
by Michael R•
Great course, but week 4 leaves out some key Spark SQL concepts that you need to finish the last project, such as the use of when(). Also, the part about DataSets is gone through rather quickly and without nearly as much detail as RDD and DataFrames.
by Ron B•
Excellent in depth explanation of RDD and the API.
Heather is super informative and the material is being passed in a practical and explanatory way.
Hopefully there will be more courses like this one about Spark Streaming and Machine Learning.