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via Udemy |
Go to Course: https://www.udemy.com/course/writing-production-ready-etl-pipelines-in-python-pandas/
Certainly! Here's a comprehensive review and recommendation for the Coursera course based on the details provided: --- **Course Review: Mastering ETL Pipelines in Python on Coursera** This course offers an in-depth, practical guide to building robust ETL (Extract, Transform, Load) pipelines using Python, tailored for aspiring data engineers and professionals aiming to automate and streamline their data workflows. Designed with a hands-on approach, it covers each critical step—from setting up your development environment to deploying scalable solutions in production. **What You Will Learn:** - **Comprehensive Toolset:** Gain proficiency using industry-standard tools like Python 3.9, Jupyter Notebook, Git, Github, Visual Studio Code, Docker, and Docker Hub. - **Essential Python Packages:** Learn to leverage powerful libraries such as Pandas, boto3, pyyaml, awscli, and more for data manipulation, cloud integration, and automation. - **Coding Paradigms:** The course introduces two fundamental approaches—functional and object-oriented programming—allowing you to choose the best suited for your projects. - **Best Practices:** Emphasis on writing clean, maintainable, and efficient code includes lessons on design principles, virtual environments, logging, exception handling, linting, dependency management, and performance profiling. - **Testing & Deployment:** Focus on unit testing, integration testing, Dockerization, and deploying pipelines in real-world environments with Kubernetes, Argo Workflows, or Apache Airflow. **Hands-On Project:** The core project involves creating an ETL pipeline that processes the Xetra dataset from Deutsche Börse Group. This involves scheduled extraction from AWS S3, data transformation and report generation, and loading the final data back into S3. The pipeline is designed to be portable and deployable across various production environments with containerization. **Course Structure & Resources:** - **Interactive Coding:** The course is rich in practical exercises requiring actual coding, ensuring you gain real-world competencies. - **Comprehensive Materials:** Free downloadable PowerPoint slides for theory lessons, detailed project code on GitHub, and a ready-to-use Docker image on Docker Hub. - **Additional Resources:** Useful links and references are provided for further exploration of each topic. **Who Should Take This Course?** This course is ideal for data engineers, backend developers, data analysts, and anyone interested in mastering the technical aspects of building and deploying ETL pipelines in Python. It is suitable for both beginners with programming basics and experienced professionals looking to deepen their knowledge. --- **Recommendation:** If you’re seeking a highly practical, well-structured, and comprehensive course that guides you through the entire ETL pipeline development process using industry-standard tools, this course is an excellent choice. Its focus on best practices, testing, deployment, and real-world datasets ensures you will walk away with skills directly applicable to professional environments. Plus, the open-access project resources make it a valuable resource for ongoing learning and project development. **Final Verdict:** Highly recommended for anyone serious about advancing their data engineering skills with Python, especially those interested in cloud deployment, containerization, and automated workflows. --- Feel free to ask if you want a more personalized review or guidance on enrolling!
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.Best practices in developing Python code will be introduced and applied: design principlesclean codingvirtual environmentsproject/folder setupconfigurationloggingexeption handlinglintingdependency managementperformance tuning with profilingunit testingintegration testingdockerizationWhat is the goal of this course?In the course we are going to use the Xetra dataset. Xetra stands for Exchange Electronic Trading and it is the trading platform of the Deutsche Börse Group. This dataset is derived near-time on a minute-by-minute basis from Deutsche Börse's trading system and saved in an AWS S3 bucket available to the public for free. The ETL Pipeline we are going to create will extract the Xetra dataset from the AWS S3 source bucket on a scheduled basis, create a report using transformations and load the transformed data to another AWS S3 target bucket. The pipeline will be written in a way that it can be deployed easily to almost any production environment that can handle containerized applications. The production environment we are going to write the ETL pipeline for consists of a GitHub Code repository, a DockerHub Image Repository, an execution platform such as Kubernetes and an Orchestration tool such as the container-native Kubernetes workflow engine Argo Workflows or Apache Airflow.So what can you expect in the course?You will receive primarily practical interactive lessons where you have to code and implement the pipeline and theory lessons when needed. Furthermore you will get the python code for each lesson in the course material, the whole project on GitHub and the ready to use docker image with the application code on Docker Hub.There will be power point slides for download for each theoretical lesson and useful links for each topic and step where you find more information and can even dive deeper.