[ FreeCourseWeb ] Udemy - Writing production-ready ETL pipelines in Python - Pandas

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[ FreeCourseWeb.com ] Udemy - Writing production-ready ETL pipelines in Python - Pandas
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 01 Introduction
    • 001 Course Introduction.en.srt (3.9 KB)
    • 001 Course Introduction.mp4 (13.8 MB)
    • 002 Links.pdf (255.0 KB)
    • 002 Task Description.en.srt (4.8 KB)
    • 002 Task Description.mp4 (22.3 MB)
    • 003 Production Environment.en.srt (2.6 KB)
    • 003 Production Environment.mp4 (6.6 MB)
    • 003 production_environment.pdf (129.2 KB)
    • 004 Task Steps.en.srt (4.6 KB)
    • 004 Task Steps.mp4 (13.7 MB)
    • 004 task_steps.pdf (92.2 KB)
    02 Quick and Dirty Solution
    • 001 Why to use a virtual environment_.en.srt (5.1 KB)
    • 001 Why to use a virtual environment_.mp4 (13.5 MB)
    • 002 Virtual Environment Setup.en.srt (6.2 KB)
    • 002 Virtual Environment Setup.mp4 (47.5 MB)
    • 003 AWS Setup.en.srt (5.6 KB)
    • 003 AWS Setup.mp4 (43.3 MB)
    • 004 Understanding the source data.en.srt (8.1 KB)
    • 004 Understanding the source data.mp4 (61.0 MB)
    • 005 Quick and Dirty_ Read multiple files.en.srt (10.9 KB)
    • 005 Quick and Dirty_ Read multiple files.mp4 (128.9 MB)
    • 005 why_to_use_a_virtual_environment.pdf (168.4 KB)
    • 006 Quick and Dirty_ Transformations.en.srt (12.4 KB)
    • 006 Quick and Dirty_ Transformations.mp4 (97.0 MB)
    • 006 links.pdf (207.9 KB)
    • 007 Quick and Dirty_ Argument Date.en.srt (8.3 KB)
    • 007 Quick and Dirty_ Argument Date.mp4 (79.7 MB)
    • 008 Quick and Dirty_ Save to S3.en.srt (6.6 KB)
    • 008 Quick and Dirty_ Save to S3.mp4 (49.8 MB)
    • 008 accessing_the_xetra_data.ipynb (1.4 MB)
    • 009 Quick and Dirty_ Code Improvements.en.srt (7.5 KB)
    • 009 Quick and Dirty_ Code Improvements.mp4 (70.3 MB)
    • 010 quick and dirty transformations.ipynb (65.4 KB)
    • 011 Quick and dirty solution - argument date.ipynb (163.6 KB)
    • 012 quick and dirty solution - save to s3.ipynb (130.4 KB)
    • 013 quick and dirty - improvements.ipynb (14.2 KB)
    03 Functional Approach
    • 001 Why a code design is needed_.en.srt (3.6 KB)
    • 001 Why a code design is needed_.mp4 (11.1 MB)
    • 002 Functional vs. Object Oriented Programming.en.srt (8.1 KB)
    • 002 Functional vs. Object Oriented Programming.mp4 (25.4 MB)
    • 003 Why Software Testing_.en.srt (5.8 KB)
    • 003 Why Software Testing_.mp4 (11.8 MB)
    • 004 Quick and Dirty to Functions_ Architecture Design.en.srt (1.3 KB)
    • 004 Quick and Dirty to Functions_ Architecture Design.mp4 (3.0 MB)
    • 005 Quick and Dirty to Functions_ Restructure Part 1.en.srt (12.6 KB)
    • 005 Quick and Dirty to Functions_ Restructure Part 1.mp4 (114.2 MB)
    • 006 Quick and Dirty to Functions_ Restructure Part 2.en.srt (11.4 KB)
    • 006 Quick and Dirty to Functions_ Restructure Part 2.mp4 (99.5 MB)
    • 007 Restructure get_objects Intro.en.srt (2.5 KB)
    • 007 Restructure get_objects Intro.mp4 (4.7 MB)
    • 008 Restructure get_objects Implementation.en.srt (9.9 KB)
    • 008 Restructure get_objects Implementation.mp4 (83.0 MB)
    • 015 functional_vs_oop.pdf (174.7 KB)
    • 015 links.pdf (216.0 KB)
    • 016 why_software_testing.pdf (120.1 KB)
    • 019 quick and dirty solution - functional.ipynb (13.4 KB)
    • 020 restructure_get_objects.pdf (55.3 KB)
    • 021 quick and dirty solution - restructure get objects.ipynb (14.0 KB)
    04 Object Oriented Approach
    • 001 Design Principles OOP.en.srt (5.7 KB)
    • 001 Design Principles OOP.mp4 (12.7 MB)
    • 002 More Requirements - Configuration, Meta Data, Logging, Exceptions, Entrypoint.en.srt (14.6 KB)
    • 002 More Requirements - Configuration, Meta Data, Logging, Exceptions, Entrypoint.mp4 (28.3 MB)
    • 003 Meta Data_ return_date_list Quick and Dirty.en.srt (13.1 KB)
    • 003 Meta Data_ return_date_list Quick and Dirty.mp4 (111.7 MB)
    • 004 Meta Data_ return_date_list Function.en.srt (11.7 KB)
    • 004 Meta Data_ return_date_list Function.mp4 (107.6 MB)
    • 005 Meta Data_ update_meta_file.en.srt (8.3 KB)
    • 005 Meta Data_ update_meta_file.mp4 (94.6 MB)
    • 006 Code Design - Class design, methods, attributes, arguments.en.srt (17.3 KB)
    • 006 Code Design - Class design, methods, attributes, arguments.mp4 (47.4 MB)
    • 007 Comparison Functional Programming and OOP.en.srt (1.4 KB)
    • 007 Comparison Functional Programming and OOP.mp4 (3.2 MB)
    • 022 design_principles_oop.pdf (152.6 KB)
    • 023 morge_requirements.pdf (252.2 KB)
    • 024 meta get_date_list quick_and_dirty.ipynb (61.7 KB)
    • 024 meta_file.csv (0.1 KB)
    • 025 meta_file.csv (0.1 KB)
    • 025 quick and dirty solution - return_date_list function.ipynb (15.0 KB)
    • 026 meta file update_meta_file.ipynb (15.9 KB)
    • 026 meta_file.csv (0.1 KB)
    • 027 code_design.pdf (170.5 KB)
    • 027 links.pdf (207.9 KB)
    05 Setup and Class Frame Implementation
    • 001 Setting up Git Repository.en.srt (4.4 KB)
    • 001 Setting up Git Repository.mp4 (34.1 MB)
    • 002 Setting up Python Project - Folder Structure.en.srt (4.0 KB)
    • 002 Setting up Python Project - Folder Structure.mp4 (18.9 MB)
    • 003 Installation Visual Studio Code.en.srt (2.6 KB)
    • 003 Installation Visual Studio Code.mp4 (15.3 MB)
    • 004 Setting up class frame - Task Description.en.srt (2.0 KB)
    • 004 Setting up class frame - Task Description.mp4 (11.2 MB)
    • 005 Setting up class frame - Solution S3.en.srt (10.4 KB)
    • 005 Setting up class frame - Solution S3.mp4 (77.4 MB)
    • 006 Setting up class frame - Solution meta_process.en.srt (1.0 KB)
    • 006 Setting up class frame - Solution meta_process.mp4 (5.7 MB)
    • 007 Setting up class frame - Solution constants.en.srt (0.9 KB)
    • 007 Setting up class frame - Solution constants.mp4 (6.8 MB)
    • 008 Setting up class frame - Solution custom_exceptions.en.srt (0.4 KB)
    • 008 Setting up class frame - Solution custom_exceptions.mp4 (1.7 MB)
    • 009 Setting up class frame - Solution xetra_transformer.en.srt (2.9 KB)

    • Description

      Writing production-ready ETL pipelines in Python / Pandas

      MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
      Genre: eLearning | Language: English + srt | Duration: 78 lectures (7h 3m) | Size: 2.43 GB
      Learn how to write professional ETL pipelines using best practices in Python and Data Engineering
      What you'll learn:
      How to write professional ETL pipelines in Python.
      Steps to write production level Python code.
      How to apply functional programming in Data Engineering.
      How to do a proper object oriented code design.
      How to use a meta file for job control.
      Coding best practices for Python in ETL/Data Engineering.
      How to implement a pipeline in Python extracting data from an AWS S3 source, transforming and loading the data to another AWS S3 target.

      Requirements
      Basic Python and Pandas knowledge is desirable.
      Basic ETL and AWS S3 knowledge is desirable.

      Description
      This course will show each step to write an ETL pipeline in Python from scratch to production using the necessary tools such as Python 3.9, Jupyter Notebook, Git and Github, Visual Studio Code, Docker and Docker Hub and the Python packages Pandas, boto3, pyyaml, awscli, jupyter, pylint, moto, coverage and the memory-profiler.

      Two different approaches how to code in the Data Engineering field will be introduced and applied - functional and object oriented programming.

      If You Need More Courses, kindly Visit and Support Us -->> https://FreeCourseWeb.com

      Thank You.



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[ FreeCourseWeb ] Udemy - Writing production-ready ETL pipelines in Python - Pandas

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2.8 GB
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[ FreeCourseWeb ] Udemy - Writing production-ready ETL pipelines in Python - Pandas

Torrent hash: 6EC5F200855A5C88EAD843DD07D0ACAFCCF24DE0