Writing production-ready ETL pipelines in Python / Pandas is the name of the training course that gives you each step to writing an ETL pipeline in Python from start to finish using tools such as Python 3.9 and Jupyter Notebook. Git, Github, Visual Studio Code, Docker, Docker Hub, and Python packages including Pandas, boto3, pyyaml, awscli, jupyter, pylint, moto, coverage, and memory profiler. Two different approaches to coding in data engineering have been introduced and applied, including functional and object-oriented programming.
The best practices in Python code development have been introduced and applied including design principles, clean coding, virtual environments, project/folder setup, settings, logging module, Exception Handling, linting tool, dependency management tool Or dependency management, performance adjustment, and optimization using profiling, unit testing module, integration testing tool and tokenization tool.
Things you will learn in this course:
- How to write professional ETL pipelines or (ETL Pipelines) in Python
- Steps of writing production-level Python code
- How to apply functional programming in data engineering
- How to design the right object-oriented code
- How to use a meta file for job control
- Coding Best Practices for Python in Data Engineering / ETL
- How to implement a pipeline in Python to extract data from AWS S3 source and convert and load data to another AWS S3 target.
Writing production course is suitable for people who:
- Data engineers, scientists, and developers who want to write professional Data Pipelines in Python.
- Anyone interested in writing data pipelines in Python is ready to go.
Course specifications Writing production-ready ETL pipelines in Python / Pandas:
- Publisher: Udemy
- Instructor: Jan Schwarzlose
- English language
- Level of training: from basic to advanced
- Duration: 7 hours and 3 minutes
- Number of lessons: 78
- Basic Python and Pandas knowledge is desirable.
- Basic ETL and AWS S3 knowledge is desirable.
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