Extract
Flatten
Polymorph
Relate
The Foundation for Semi-Structured Data Processing
Discover a unique and revolutionary approach to normalizing complex semi-structured data in minutes instead of months.
DataPancake™ (formerly Pancake) is a data engineering tool that makes complex semi-structured data pipelines easy to build and downstream processes impossible to break. It automates the parsing, extraction, relating, and flattening processes for semi-structured data, transforming raw semi-structured data into Dynamic Tables to produce relational data streams with zero coding.
"Most Snowflake customers struggle with this issue, and all of our SEs have had to deal with it. DataPancake will end up saving data engineers months of time."
Peter M, Senior Startup Program Lead, Snowflake
Schema Discovery & Analysis
Analyses that give you unprecedented visibility into unlimited levels of your nested structures including every attribute’s polymorphic versions.
DataPancake processes millions of rows in minutes to provide you with in-depth, investigative analyses and keep costs under control.
Relational Data & Dynamic Table SQL Generation
Turn complex semi-structured data into truly relational streams you can use to find buried insights in even the most complex data sources.
DataPancake’s configuration-based approach accurately generates production-ready SQL code to implement Dynamic Tables.
Monitoring & Alerts
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DataPancake makes it effortless to detect changes and review schema evolution at custom scheduled intervals or on-demand.
It helps you eliminate data loss and inaccuracy caused by transformation logic errors used to process polymorphic data, and you can set up alerts to inform you any time there's a change to the schema.
Stream Platform Support
DataPancake can connect to kafka message streams piped into Snowflake and configured to only scan records since the last scan, saving time and controlling costs.
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Contact us to learn more about how you can set up semi-structured data pipelines in minutes instead of months
Processing semi-structured data can be a headache thanks to inconsistent documentation, poor data governance practices, polymorphic data, and schema drift.
With DataPancake’s revolutionary approach, you can scan, analyze, and generate SQL to extract and flatten semi-structured data into relational and nested Dynamic Tables in minutes, not months
Processing schemaless semi-structured data can be challenging. DataPancake makes it simple.