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via Udemy |
Go to Course: https://www.udemy.com/course/writing-tests-for-simeng-python-code-conversion-concepts-101/
Certainly! Here's a detailed review and recommendation for the Coursera course titled "Writing Tests for SimEng - Code Conversion Concepts." --- **Course Review: "Writing Tests for SimEng - Code Conversion Concepts"** The course offers a comprehensive guide to writing and managing unit tests within a Python environment, specifically targeting PySpark SQL applications. It is an excellent resource for data engineers, data scientists, and software developers who work with Spark and Python, aiming to improve their testing and debugging skills. **What You Will Learn:** - How to write effective unit tests for existing code in Python and PySpark SQL. - Utilizing Excel Power Queries for data validation and sharing results with team members. - Setting up and configuring development environments in PyCharm, including virtual environments (venvs), testing frameworks, and code coverage tools. - Developing code for various functional workflows, from small functions to large classes. - Establishing environments and variables locally for consistent testing. - Understanding the code release process and managing environment variables. - Practical debugging techniques such as breakpoints, step-in/over, and output comparison. - Creating Spark SQL views with Hive integration, involving Hadoop and Spark setup. - Ensuring tests mimic production Spark environments to prevent errors during automated builds like Jenkins. **Content Quality and Structure:** The course provides a balanced mix of theory and practical exercises. It starts with setting up the necessary tools and environments, which is crucial for efficient testing and debugging. The inclusion of Excel Power Queries adds real-world relevance, demonstrating how small data manipulations can play a role in validating larger workflows. The section on Spark session creation and Hive integration is particularly valuable, as many learners face challenges with Spark environments. Debugging practices—such as breakpoints and output comparison—are well-covered, enabling learners to troubleshoot effectively. **Strengths:** - Practical, hands-on approach with clear instructions. - Covers setup, testing, debugging, and release processes holistically. - Very useful for teams working in environments where Spark SQL and Python are combined. - Emphasizes mimicking production Spark environments to prevent deployment errors. **Areas for Improvement:** - The course could benefit from more real-world project examples. - Additional coverage on advanced testing strategies like mocking and test-driven development (TDD). - Some learners might find the technical setup (Hadoop, Spark, Hive) daunting initially, but detailed guidance mitigates this. **Final Recommendation:** I highly recommend this course to professionals involved in Spark and Python development who want to improve their testing, debugging, and environment configuration skills. It is especially valuable for teams aiming to streamline their code release process, ensure code quality, and prevent deployment errors. The practical nature of the content, combined with step-by-step instructions, makes this course a valuable addition to any data engineering or data science toolkit. --- Would you like a concise summary or a short endorsement to share?
Writing Tests for SimEng - Code conversion conceptsWrite unit test for prevailing code in Python Pyspark SQL and configure tests in PycharmWhat you will learn:How to write unit test for prevailing code in Python Pyspark SQLHow to use Excel power queriesHow to setup Pycharm, venv, unit testing, coverage for testingHow to write code for smaller and bigger function class task workflowsHow to setup correct venv locallyIntro to code release processSetting up local env variablesTopics:In Excel power queries we use mostly group by and join on small data to check our results and share with seniors.We can create a simpler notebook of the same logic of the codeA lot of type the errors are due to Local Spark sessions which can be created in many many ways, but if we want to create spark sql views which uses hive then we have to install a hadoop and spard by downloading them.Debugging and creating break points and then using step into / step over to generate the output and match or save it with excel Making sure we mimic spark from older tests because otherwise we can get error in Jenkins build