Most Snowflake customers struggle with this issue, and almost all of our SEs have had to deal with it. DataPancake saves data engineers weeks of time, money, and pain.
Cameron Wasilewsky, Technical Lead
Snowflake Startup Accelerator
Accelerate Your Data Pipeline with DataPancake
10 Minutes
Average time to scan 1 billion rows
10x ROI
Demonstrated across compute, engineering, and data quality outcomes
10+ Industries
Financial services, healthcare/life science,
public sector, manufacturing, retail and more
Structural assurance is only meaningful if it's verifiable, configurable, and maintainable at enterprise scale.
Every capability below is designed with that constraint in mind, not as a feature added to a tool, but as a component of a complete assurance architecture.
Are You Ready To Pancake Your Data™?
DataPancake is the only Snowflake Native App designed to take you from raw, complex and deeply nested semi-structured data to accurately flattened, normalized, enriched, secure, documented, and GenAI-ready relational Dynamic Tables and Views, with zero technical debt in minutes not months.
Schema Discovery
Key Features:
Recursively scans 100% of your semi-structured data (JSON, XML, Avro, Parquet, and ORC) to discover all attributes including: nested arrays, objects, and every polymorphic version of each attribute
Detects all 7 polymorphic data type variations (4 primitives, 2 types of arrays, and objects)
Identifies escaped JSON within string fields
Scanning and discovery benefits from Snowflake's vertical scaling
Infers Snowflake destination data types including correct datetime formats for accurate type conversion
Pipeline Design
Key Features:
Enables users to customize how each pipeline SQL DDL will be generated:
Configure foreign key relationships for nested arrays
Apply column-level transformation logic during the materialization process
Create virtual attributes for derived fields or semantic model metrics and filters
Configure row access and column masking policies integration
Configure semantic layer of views including additional column level transformation
SQL Code Generation
Key Features:
Generates SQL DDL code needed to create relational dynamic tables and policy infused views in Snowflake, based on your configured attribute metadata
Code generated Snowflake Dynamic Table SQL DDL using DataPancake ITDCs™ (Immutably Typed Derived Columns™) to create technical-debt free pipelines
Reflects configured transformations, foreign keys, and virtual attributes allowing for post-normalized table joins
Code generated Views selecting data from normalized dynamic tables that incorporate row-access and column-masking security policies and additional column level transformations
Code generated streams, tasks, and tables to track Dynamic Table metadata including insert and last updated datetime
Schema Drift Monitoring
Key Features:
Continuously monitors and alerts you when your semi-structured data source schema changes
Detects schema drift in semi-structured data sources like JSON and XML
Flags changes in data types, structure, and new attributes
Alerts users to configure newly discovered attributes and regenerate pipeline code
Optionally generates updated pipeline SQL DDL upon schema change detection
Data Dictionary Builder
Key Features:
Creates a comprehensive data dictionary that includes definitions, synonyms, and sample values for every attribute with integration to our Semantic Model Generator for Cortex Analyst
Use your preferred LLM to generate definitions, synonyms, and sample values.
Extend DataPancake's system prompt with your own custom context for greater clarity and improved responses
Generate descriptions for the datasource, nested arrays, and attributes
Semantic Model Generator
Key Features:
Generates Cortex Analyst-ready YAML files that define your complete semantic model
Automatically includes relationship metadata based on selected columns
Integrated with the Pipeline Designer and Data Dictionary Builder
Configure custom metrics, facts, and filters through virtual attributes, then add verified queries and custom instructions.
Pancake Your
Streaming Data
With Ease
DataPancake includes native support for Kafka topics streamed into Snowflake, giving you full control over complex, high-volume data in motion, without the overhead.
Discovery of Stringified JSON
Accurate schema discovery of complex escaped message strings
Deduplication Logic
Includes configurable logic to materialize the most recent message per message key
Flattened Message Metadata
Normalized dynamic tables and views can be configured to include flattened Kafka-specific metadata (e.g. topic, partition, offset, timestamp)
Incremental Scanning
Recurring scans use the last processed message timestamp for incremental data scanning, reducing compute cost
"DataPancake has proven to be a superior tool. Its adoption has lead to substantial savings in engineering resources, cloud compute costs, and many other areas and has accelerated our adoption of GenAI."
Scott Hamilton
Data Engineering Manager
