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Distributed Computing with Python

Track :

Programming

Lessons no : 7

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What will you learn in this course?
  • Design and implement distributed computing systems using Python to optimize performance and scalability in real-world applications
  • Identify and address Python's Global Interpreter Lock (GIL) limitations in distributed system development
  • Utilize Python libraries and frameworks such as MPI, Dask, and PySpark for distributed data processing and computation
  • Develop scalable, fault-tolerant distributed applications with Python for big data and cloud environments
  • Apply best practices for concurrency, parallelism, and multiprocessing in Python to enhance distributed system efficiency
  • Troubleshoot and optimize distributed Python applications to improve performance and resource utilization

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Lessons | 7


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Yash Arun 

it was soo good
2025-10-13

Krushna Koshatwar 

good
2025-09-06

T.Dhanusiya 

Clear explanations kept me learned so clearly
2025-09-01

Fauzia Habib 

Good
2025-06-02

Mistiey Mistiey 

good
2025-05-19

Yash Rane 

-
2025-04-16

Yash Deshmukh 

Good
2025-04-16

Bhawani Nehra 

good
2025-04-16

Janhavi 

Great
2025-04-16

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good
2025-04-16

Sairaj Dalvi 

nice Course
2025-04-15

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great
2025-04-02

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Now it turns out that Python specifically has issues with performance when it comes to distributed systems because of its Global Interpreter Lock (GIL). This is basically the soft underbelly of Python that only allows for a single thread to be controlled by the interpreter at a time .