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What are the key differences between Microsoft Data Fabric and legacy data wareh
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What are the key differences between Microsoft Data Fabric and legacy data wareh
As organizations move toward cloud-first and AI-driven data strategies, many are evaluating Microsoft Data Fabric as an alternative to traditional data warehouses. While legacy warehouses have long supported reporting and BI, modern platforms promise greater flexibility, scalability, and intelligence.
I’d like to invite the community to share insights and real-world experience on this topic.
Key discussion points:
- How does the architecture of Microsoft Data Fabric differ from traditional data warehouses?
- What are the main benefits of Microsoft Data Fabric for analytics, AI, and real-time insights?
- How does data integration, governance, and security compare?
- In what scenarios does Microsoft Data Fabric outperform traditional warehouse solutions?
- What challenges or limitations have you seen when adopting Microsoft Data Fabric?
Why This Discussion Is Important
Traditional data warehouses are often siloed and batch-oriented, whereas Microsoft Data Fabric aims to unify data engineering, integration, analytics, and BI into a single cloud-native experience. Understanding these differences can help organizations choose the right data platform for the future.
Your input matters
Have you worked with Microsoft Data Fabric or traditional data warehouses? Share your perspective, use cases, or best practices in the comments below.
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