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99% improvement in data availability

83% improvement in data processing times

Speedy data processing utilizing iterator components


Red Classic Transportation needed to enable more timely information into Snowflake. Once a day refreshes were not enough.  

Source systems with limited or non-existent audit and change tracking were limiting the ability to track changes (including deletes) and the team wanted to streamline ELT processes to simplify errors and error handling.


To effectively identify and extract the necessary tables and variables, Red Classic overcame a unique challenge. Traditional high-water mark columns in the source system lacked consistency, prompting them to uncover the solution in the MSSQL row version system updated column. They successfully converted it to BIGINT, ensuring seamless integration. They appended all tables in a persistent storage (PSA) layer, facilitating effortless querying without flattening. With load dates added, an audit trail was formed. A SQL query crafted through these tools enabled them to maintain the production layer as a mirror image of their source systems and incorporate API calls to monitor and terminate runaway processes.


  • The solution took the business from data that was up to 24 hours stale down to 15 minutes.
  • Processing times were notably shortened, going from nearly an hour to under 10 minutes.
  • Previous failures of ELT processes accompanied by unclear error messages were completely eradicated, ensuring uninterrupted operation.
  • Enhanced audit and logging capabilities saved significant time for BI and IT teams.
  • Data reliability significantly improved
    • Hard deletes were effectively addressed through the implementation of primary key checking and row version.