Bad Ingestion Architecture Generates Million Dollar Snowflake and Databricks Bills
The end of the month arrives, and the data engineering team is called into an emergency meeting with the CTO and CFO. The cloud data warehouse bill has spiked by 40%, crossing the million-dollar threshold for the quarter. The immediate reaction from leadership is almost always to blame the data consumers. They assume a data scientist ran a rogue, unoptimized machine learning model, or a business intelligence analyst executed a massive cross-join across production tables.
But when you open the query history and look at the cost attribution, the truth is far more damning. The analytics queries are running efficiently. The massive credit consumption isn't coming from the consumption layer at all. It is coming from the ingestion layer.
In the era of cloud data platforms like Snowflake and Databricks, compute is decoupled from storage. This architecture is incredibly powerful, but it hides a massive trap. Storage is cheap, but...
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