Enroll Course: https://www.udemy.com/course/writing-production-ready-etl-pipelines-in-python-pandas/
In the ever-evolving landscape of data engineering, the ability to build robust and efficient ETL (Extract, Transform, Load) pipelines is paramount. If you’re looking to elevate your Python skills and venture into production-level data processing, the Udemy course ‘Writing production-ready ETL pipelines in Python / Pandas’ is an absolute game-changer.
This comprehensive course takes you on a hands-on journey from the ground up, guiding you through every crucial step of crafting an ETL pipeline. It leverages a powerful stack of industry-standard tools, including Python 3.9, Jupyter Notebook, Git and GitHub, Visual Studio Code, and Docker with Docker Hub. You’ll also become proficient with essential Python packages like Pandas, boto3, pyyaml, awscli, jupyter, pylint, moto, and memory-profiler.
What truly sets this course apart is its dual approach to coding methodologies. You’ll explore and apply both functional and object-oriented programming paradigms, providing you with a versatile toolkit for tackling diverse data engineering challenges. The instructor meticulously covers best practices in Python development, ensuring your code is not only functional but also clean, maintainable, and scalable. This includes deep dives into design principles, virtual environments, project setup, configuration management, logging, exception handling, linting, dependency management, performance tuning with profiling, unit testing, integration testing, and finally, dockerization.
The practical application of these concepts is centered around the Xetra dataset. This real-time trading data from Deutsche Börse Group, available publicly on AWS S3, serves as the perfect playground. You’ll learn to extract this data, perform transformations to create insightful reports, and load the processed data into another AWS S3 bucket. The pipeline you’ll build is designed for seamless deployment in any production environment capable of handling containerized applications, with a clear path towards orchestration tools like Argo Workflows or Apache Airflow.
The course structure is highly engaging, blending practical, interactive coding sessions with necessary theoretical explanations. You’ll receive the Python code for each lesson, the complete project on GitHub, and a ready-to-use Docker image on Docker Hub. Additionally, downloadable PowerPoint slides for theoretical lessons and curated links for further exploration are provided, allowing you to truly master each topic.
If you’re serious about becoming a proficient data engineer and want to build ETL pipelines that are production-ready, reliable, and scalable, this course is an invaluable investment. It equips you with the knowledge and practical experience needed to excel in the field.
Enroll Course: https://www.udemy.com/course/writing-production-ready-etl-pipelines-in-python-pandas/