Course · Advanced
Parallel Processing for Data Engineering
Modern data engineering often requires processing volumes of data that exceed the limits of single-threaded programs. This course introduces parallel processing concepts that allow you to break problems into smaller pieces and execute them efficiently across multiple processors. By understanding MapReduce and parallel execution models, you’ll be better equipped to design scalable data pipelines and tackle performance bottlenecks in real-world systems.
- Advanced friendly
- 5 hrs
- 4 lessons
- 1 project
- Premium
Course overview
Scale data processing workflows by applying parallel processing and MapReduce techniques to efficiently analyze large datasets.
What's inside
4 lessons · 1 project
- 01 59 minIntroductions to Parallel Processing
Learn how use multiporcessing in Python.
- 02 60 minProcess Pool Executors
Learn how to use process pool executors to run a function in parallel and retrieve the results.
- 03 56 minIntroduction to MapReduce
Learn how to implement map reduce using a process pool.
- 04 50 minProcessing Data with MapReduce
Learn how to process data with MapReduce by analyzing a large set of English words.
- 05 23 minGuided Project: Analyzing Wikipedia Pages Project
For this project, you'll be a data analyst using Python and MapReduce to search Wikipedia articles, building efficient text analysis skills for real-world datasets.
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