Great question! I would say it boils down to two primary things: 1) internal buy-in and adoption for these tools and 2) managing data debt as your new stack scales. Re: 1), I would encourage you to hold regular training sessions or surface materials that highlight why you're investing in this new tool, how it brings value, and how to quickly onboard and get started. Most vendors will offer this for paid solutions; for open source options, there's usually great documentation and tutorials. Re: 2), to manage the inevitable proliferation of dbt models, Airflow jobs, and more, I'd lean on your data observability layer to ensure that your stack is being monitoring and you know how these solutions are impacting downstream systems. Observability also helps identify monitoring gaps to prevent outdated or stale jobs and models from impacting your data products in a negative way. Hope that's helpful!