aws-samples / datatype-validation-with-apache-spark
Data type and structure validation framework for delimited data using Apache Spark that validates input data against expected schema including number of columns, data types, nullability and assigns error flag and error message column to label specific error details
Summary
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| Main Code: 497 LOC (8 files) = SCALA (95%) + SBT (4%) | |||
| File Size: 0% long (>1000 LOC), 100% short (<= 200 LOC) | |||
| Unit Size: 0% long (>100 LOC), 56% short (<= 10 LOC) | |||
| Conditional Complexity: 0% complex (McCabe index > 50), 74% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (5 components) | ||
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less than a month old
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0% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits. |
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| Goals: Keep the system simple and easy to change (4) | |||
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Features of interest:
TODOs
2 files |
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Latest commit date: 2021-08-09
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