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technologyreview.com·2h ago
Data Science
Towards Data Science·Jun 12·5 min read
I Thought Data Engineering Was Just Writing Scripts. I Was Wrong.
TL;DR
A gap in understanding data engineering emerged when the author realized that building a production-ready ETL (Extract, Transform, Load) pipeline involves more than just scripting. They built an ETL pipeline using the GitHub API, which initially worked but broke when pushed to production.
✦ Why It Matters
Engineers must prioritize error handling and state management when developing production-ready data pipelines.
Key Takeaways
- Implement robust error handling in data pipelines.
- Ensure state management for data retention.
- Test pipelines thoroughly before production deployment.
- Document the data engineering process for future reference.
Full Summary
The author, a data analyst transitioning to data engineering, initially believed that creating an ETL pipeline was straightforward, involving data extraction, cleaning, and loading. They constructed their first pipeline using the GitHub API, which successfully pulled and saved data to a CSV file.
However, when attempting to enhance the pipeline for production use, they encountered significant issues that revealed critical gaps in their understanding. Key problems included the lack of memory in the pipeline, which meant it could not retain state or handle errors effectively.
This experience underscored the necessity of implementing robust error handling and state management in data engineering. The findings suggest that data engineers must go beyond basic scripting to ensure reliability and efficiency in data workflows.
Ultimately, the author learned that production-ready pipelines require a deeper understanding of data flow and system resilience.