quality_assurance · saas · workflow
GoDaddy builds a hybrid LLM and Spark synthetic data generator to eliminate test data bottlenecks
GoDaddy's data teams lacked sufficient, realistic test data for validating pipelines before production. Using real production data in lower environments posed privacy and security risks, while manually crafting test data was slow, costly, and impossible to maintain across hundreds of schemas.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Schema submitted via API
A producer or consumer submits a schema via the Data Lake API with parameters including schema definition, target bucket path, row count, partitions, and date range.
Tools used
GoCodeDatabricks Labs DatagenEMR ServerlessLambdaDynamoDBS3SparkDLMSDeX
Outcome
Since launching, GoDaddy achieved a 90% reduction in time spent creating test data, 100% elimination of production data in test environments, and 5x faster pipeline development cycles.
What failed first
Prior approaches all proved unworkable: engineers spent days on manual JSON and SQL scripts that could not scale; off-the-shelf generators failed on complex schemas; and pure LLM generation was prohibitively expensive and slow at row-scale.
Results
Time saved90%
Volume100%
Cost replaced80%
Grounding & classification
Source type: technical build writeup
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code generationdata extractionfailure mode describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedsoftwarecost reductioncycle time reductionemployee productivitytime savedtechnical build writeupquality assuranceagentic task execution