I've recently faced some challenges after transitioning away from a traditional data warehouse in our data pipeline. Initially, I thought this shift to using data fabric, specifically with Azure, would streamline things, but I'm realizing that it hasn't replaced the warehouse as I expected. It feels chaotic, like we're setting ourselves up for future disorganization. I'm really trying to understand if there's something I'm missing. What strategies or tools do organizations use to keep their data organized in this new landscape? Is data fabric just creating more disarray, or are there best practices to follow? I believe the warehouse still plays a crucial role, focusing on core data while medallions might be more about enriched datamarts. How are companies balancing this new approach?
2 Answers
In my experience, data fabric often ends up being just a centralized spot for Power BI, especially in larger organizations with around 100K users. The challenge is that there isn't always a dedicated team managing these tools, which can lead to confusion. Recent improvements like a direct connection to databricks really helped us, but that’s time-consuming to set up. Lack of management can be a big issue, leaving people overwhelmed with handling everything. You’re right to question the balance between speed and organization!
I totally get your concerns! It’s essential to differentiate between Azure and Fabric when it comes to organization. Azure offers structured options like management groups and resource tagging that help keep things in check. However, Fabric can get unwieldy since it's more about flexibility, and there’s less control over things like IAM and policies. Your idea of using a bronze-silver-gold model could really help structure your data better! So, bronze could be where raw data sits, silver for the processing phase, and gold for your warehouse. Keeping a clear process like this might prevent the chaotic feel you're worried about.
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