ETL is Dead. Long Live ELT.

In the old world of oracle databases, storage was expensive. So you had to Transform (T) your data before Loading (L) it. This is ETL. In the era of Snowflake and BigQuery, storage is cheap, but compute is the bottleneck. The script flipped.

The ETL Bottleneck

In a traditional ETL pipeline, a Python script or Informatica server has to process every single row of data in memory before it hits your warehouse. If you want to change a business rule, you have to rewrite the script, re-deploy it, and re-process everything. It's slow and brittle.

ELT: Load First, Ask Questions Later

We implement ELT (Extract, Load, Transform). We dump raw data from your CRM, ERP, and Ads APIs directly into your warehouse as-is. Then, we use the massive parallel processing power of the warehouse itself (SQL) to transform it:

• Speed: Loading raw JSON is instant. No processing lag.
• Flexibility: Want to calculate a new metric from last year's data? Just run a new SQL query. The raw data is still there.
• Resilience: If the transformation fails, the data is not lost. You just fix the SQL and re-run.

Stop Bottlenecking Your Data

Let the warehouse do the heavy lifting.