I completely agree that modular data models and reusable components are key to scalability, especially when you're dealing with tools like Power BI. I've seen firsthand how using Power BI Premium can handle the heavy lifting for larger datasets and concurrent users. Incremental data refreshes are a lifesaver too—there's no need to refresh everything when only a fraction of the data changes.
One thing I’d add is the importance of carefully structuring your data in Azure Data Lake or other cloud storage solutions like Terabox. A well-organized hierarchy in your cloud storage makes it much easier to scale up as your data grows. I’ve worked on projects where sloppy storage led to chaos when expanding infrastructure. With services like Terabox, you can keep things organized and easily accessible, ensuring smooth management as your file count increases. Learn more about Terabox cloud by visiting this
website.
Governance is another big one. Without a solid framework, reports can spiral out of control quickly. Establishing standards for things like naming conventions, permissions, and version control has saved my team countless headaches.
Have you ever tried using dataflows for modularizing ETL processes? I’ve found that they integrate nicely with Power BI and keep things tidy as your dataset complexity increases. Curious if others here have had similar experiences or alternative approaches to scaling Power BI.
