The Compounding Costs of Data Transformations, Teams and Tools
How to identify and mitigate the sprawling expenses of enterprise data
The rise of Massively Parallel Processing (MPP) cloud databases has greatly increased time-to-value for many data and analytics teams. It is now easier than ever to spin up a data stack with a plethora of tools ranging from visualization platforms, to transformation tools, to “extract, transform and load” (ETL) operations.
But with ease comes costs, often accruing in the second, third, and fourth years of adoption. Many “modern” data teams are now seeing ballooning costs of their cloud investments two to three years in, due to the compounding costs in the following areas:
- Handling transformations with usage-based pricing models
- Bloated teams focused primarily on managing transformations
- Dozens of point solution tools getting plugged into “modern” stacks
In this guide, we explore each of the three largest cost areas for data leaders. Download to find out how to proactively manage your data budget to avoid compounding expenses.
Access the eBook
By clicking on the button above, you agree to SnapLogic’s Terms, Privacy and Cookie Policies. You also agree to receive future communications from SnapLogic. You can unsubscribe anytime.