AWS Glue ETL Design Principles for Production PySpark Pipelines
It is significantly harder to build one that stays maintainable as logic grows, performs reliably at scale, and does not quietly accumulate operational debt over time.
Useful takeaways from this story.
It is significantly harder to build one that stays maintainable as logic grows, performs reliably at scale, and does not quietly accumulate operational debt over time.
AWS Glue makes it easy to get a PySpark pipeline running quickly.
Most Glue pipelines start simple and become difficult to manage gradually — formulas get hardcoded, modules grow without boundaries, output files proliferate, and before long a single job is doing too many...
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The useful part
It is significantly harder to build one that stays maintainable as logic grows, performs reliably at scale, and does not quietly accumulate operational debt over time. AWS Glue makes it easy to get a PySpark pipeline running quickly. Most Glue pipelines start simple and become difficult to manage gradually — formulas get hardcoded, modules grow without boundaries, output files proliferate, and before long a single job is doing too many things in ways that are hard to test, hard to debug, and expensive to change.
Details worth keeping
AWS Glue makes it easy to get a PySpark pipeline running quickly. Most Glue pipelines start simple and become difficult to manage gradually — formulas get hardcoded, modules grow without boundaries, output files proliferate, and before long a single job is doing too many things in ways that are hard to test, hard to debug, and expensive to change.
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