Custom Great Expectations
While the platform provides powerful built-in PII and basic analytics masking, you can inject standard Great Expectations (GX) Python architectures directly into the centralized pipeline hook architecture.
Integrating Remote Contexts
If your organization already possesses an established Great Expectations deployment mapping directory, simply authorize the API context through our environment variables page. DataFlow AI will immediately begin indexing your pre-existing rulesets.
Pythonic Quality Validation
Our proprietary Data Contracts AI executes the vast majority of standardized tests instantly (e.g. is_not_null, is_unique, is_email_format). However, massive financial institutions and nuanced healthcare organizations often maintain hundreds of thousands of lines of deeply complex legacy Python code written strictly for the Great Expectations platform. DataFlow AI securely imports these constraints as a natively unified testing module.
Asynchronous Evaluation
Any Pipeline Node transitioning downstream from the raw Bronze layer to the validated Silver layer triggers a seamless asynchronous hook.
This hook securely parses and evaluates against the remote Great Expectations Dataset Dictionary without pausing the primary ingestion stream, maximizing cluster utilization.
Native Parallel Execution
Advanced GX expectations containing dense mathematical conditional parameters (such as expect_column_bootstrapped_median_to_be_between) run simultaneously distributed across your auto-scaled Apache Spark worker nodes natively.
Additionally, all resultant GX validation graphs map identically back into DataFlow's central Data Catalog dashboard.
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