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From the testing perspective, we did data comparison on both sides to ensure the total numbers and data were properly loaded to MongoDB.
We also faced a number of challenges during the testing as this is a new field for the traditional DBMS testing. With the mapping lineage from source to target, we can et the ETL correctness field by field and compare the row numbers.
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During the test execution, the main objective is to ensure that all rows in MySQL are properly transformed, aggregated and loaded to MongoDB. There are several testing points where we should apply the specific comparison and validation on.
Field by field check. Total number counting validation by some fields. For example, if there is a sales table in MySQL, we can count the total rows group by region to learn the sales total count for each region.
In the meantime, in MongoDB we could apply the following query syntax to get the same effect. Aggregation rule validation during ETL. This is a complex case, but the idea is similar to what we did in 01–1 traditional DBMS aggregation. To test it, we could introduce a staging table in MongoDB to reflect the source data.
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We also faced some challenges going forward since most of the steps performed are semi-automated execution. It requires us to apply the complex testing validation rule during the test.
If there are videos or images in the source system and they can be loaded to MongoDB as binary, this is another challenge to test it. This site uses Akismet to reduce spam.
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High Tech Life Sciences Manufacturing. 011-1 We also faced some challenges going forward since most of the steps performed are semi-automated execution. Leave a Reply Cancel reply.