Microsoft Fabric End-to-End: From Raw Data to Business Decisions with Amit Chandak [MVP]

Microsoft Fabric brings data engineering, analytics, business intelligence, governance and increasingly AI together in one platform. But what does an end-to-end Fabric architecture actually look like when you move beyond individual features and start connecting everything?In this episode of the M365 FM Podcast, Mirko Peters is joined by Amit Chandak [Microsoft Data Platform MVP] for a practical journey through Microsoft Fabric — starting with raw organizational data and ending with trusted information that business users can use to make decisions.

WHY MICROSOFT FABRIC?

Before Fabric, organizations could already build sophisticated analytics architectures using Azure, Power BI and other platforms. The problem wasn't a lack of technology. In many cases, it was the opposite: organizations had too many choices, separate storage technologies, different compute models and multiple copies of essentially the same data.Amit explains how Microsoft Fabric attempts to simplify this architecture by bringing workloads together around shared foundations such as OneLake, common Fabric capacity and the Delta format. Lakehouses, warehouses, Power BI and other Fabric experiences can therefore operate as parts of a broader platform instead of completely isolated services.

ONELAKE AS THE FOUNDATION

OneLake is one of the central concepts behind Fabric. Amit compares it conceptually to OneDrive: instead of every analytics workload creating completely independent storage environments, OneLake provides a virtualized storage foundation across the Fabric tenant.Organizations can still separate data through workspaces, Lakehouses, Warehouses and domains, but those resources exist within a common Fabric storage architecture. This becomes particularly important when organizations want to reduce unnecessary duplication while maintaining security and organizational boundaries.

CENTRALIZED DATA OR DATA MESH?

Fabric doesn't automatically mean putting everything into one giant centralized analytics environment.For smaller organizations, a centralized architecture may still work well. As organizations become larger, Amit sees increasing value in domain-oriented architectures where areas such as sales, finance and purchasing can have their own workspaces and responsibilities.IT can remain responsible for availability, governance and the technical foundation while business domains increasingly take ownership of how their data is analyzed and consumed.

SHORTCUTS INSTEAD OF COPYING DATA

One of the recurring themes throughout the conversation is avoiding unnecessary copies of data.Fabric Shortcuts allow teams to reference data stored elsewhere rather than physically copying it into every environment that needs it. That can apply both inside Fabric and to supported external storage.Amit also explains an interesting architectural benefit of shortcuts: they can help separate workloads across capacities. This can become important when organizations want Power BI consumption workloads isolated from intensive data engineering workloads while still working with the same underlying information.

LAKEHOUSE VS. WAREHOUSE

One of the biggest Fabric architecture questions remains: Should you use a Lakehouse or a Warehouse?A Lakehouse can work with structured and unstructured data and is naturally aligned with Spark. A Fabric Warehouse focuses on structured data and provides the familiar T-SQL experience.Both ultimately use Delta for structured data inside Fabric, which means the decision increasingly comes down to the type of data, preferred technologies and workloads.Organizations with strong SQL teams don't necessarily need to abandon their existing skills. Teams working with very large datasets, advanced engineering scenarios, unstructured information or extensive data science workloads may find the Lakehouse and Spark approach more attractive.

GETTING DATA INTO FABRIC