A hands-on workout in Hadoop, MapReduce and the art of thinking "parallel"
InstructorLoony Corn
Free
Free
Free
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This instructional slide introduces the concept of parallel processing through examples like friend recommendations and inverted indexes, illustrated by a detailed MapReduce workflow diagram.
This course is taught by a four-member team with experience at Google and Flipkart, offering a hands-on workout on Hadoop, MapReduce, and the art of big data.
The presenter introduces the practical environments for the course, highlighting hands-on exercises conducted in both local setups and cloud infrastructure.
Instruction covers sophisticated optimization techniques such as Total Sort, Secondary Sorting, and the use of the Streaming API.
The curriculum implements practical algorithms including Inverted Indexing, Recommendation Systems, and K-Means Clustering.
Learner signals indicate inconsistencies in pacing and potential difficulties understanding the instructor's diction.
Setup instructions may require manual adjustments for different Java versions or operating systems like Windows.
The curriculum aligns with the technical requirements of Java-literate engineers and analysts.