Category Archives: Microsoft

Reusable enterprise metrics are not created by drawing a clean semantic-model diagram. They emerge when the organization can define one business calculation, expose it through a governed semantic model, reuse it across reports and analytical tools, change it without surprising consumers, and prove that the result still means what stakeholders think it means. Those responsibilities […]

A Fabric capacity incident often begins with a user symptom: reports suddenly wait, interactive requests are delayed or rejected, a background job fails to start, or an administrator sees utilization above the expected range. The wrong first response is to scale immediately. For DP-600, the stronger operating skill is to read the workload, identify what […]

Fabric Git integration gives supported workspaces a source-control boundary: developers can commit supported Fabric items, compare workspace and repository state, update the workspace from Git, and use branches to collaborate. For DP-600, the real engineering question is how that version history connects to testing, deployment, ownership, rollback, and production evidence. The core Git version-control model […]

Power BI and Microsoft Fabric are often shown as a clean diagram: data lands in OneLake, engineering shapes it, semantic models define business meaning, and reports deliver insight. The diagram is useful and incomplete. In a real production system, scale, permissions, capacity, data freshness, deployment, lineage, and ownership determine whether that integration remains reliable. Those […]

A team choosing a real-time architecture in Fabric can plausibly start from two directions. One may favor Real-Time Intelligence with Eventstream, Eventhouse, KQL, Real-Time dashboards, and Activator because event latency and continuous operations are central. Another may favor landing data into OneLake and serving analytics through lakehouse, warehouse, and Power BI patterns because the primary […]

Semantic-model performance work should begin with one question: what is the user actually waiting for? A slow visual can be caused by DAX, model shape, relationship propagation, storage mode, source execution, Direct Lake fallback, capacity pressure, rendering, or several of those at once. For DP-600, optimization is not a bag of tricks; it is a […]

Fabric lineage view answers a deceptively difficult question: how does data move from upstream sources through Fabric items to downstream analytical consumers? Impact analysis asks the complementary question: if an item changes, what could be affected? Both are explicitly relevant to DP-600, but the real value comes from understanding what the graph represents and what […]

Fabric deployment pipelines are a release mechanism, not a replacement for source control or testing. They move supported content through defined stages, typically development, test, and production, while maintaining item relationships and applying deployment rules. In the DP-600 lifecycle, the important question is how versioning, validation, promotion, rollback, and observation fit together around that mechanism. […]

Fabric workspace governance cannot be solved by choosing the right role names and walking away. A workspace is both a collaboration boundary and a permission boundary, and the decisions around Admin, Member, Contributor, Viewer, item sharing, Git integration, capacity, and data access determine how safely analytics assets evolve. That governance work is explicit in DP-600. […]

Row-level security and object-level security protect different parts of a semantic model. RLS filters which rows a user can query. OLS can hide entire tables or columns, including their metadata, from users who are not allowed to see them. For DP-600, configuring the roles is only the beginning; the control is trustworthy only when its […]

Calculation groups solve a real semantic-model problem: the same transformation is often applied across many measures. Year-to-date, prior-period, percentage change, currency presentation, and scenario logic can multiply the number of measures in a model even when the underlying business rule is identical. For DP-600, the important skill is not simply knowing that calculation groups exist, […]

DAX optimization becomes dangerous when it starts with formula folklore. Replacing one function with another, adding variables everywhere, or avoiding iterators categorically can produce code that looks ‘optimized’ without improving the workload users actually run. For DP-600, enterprise semantic-model performance is explicitly a measured engineering responsibility, and DAX is only one part of that system. […]

Microsoft Fabric makes both Lakehouse and Warehouse first-class analytical data stores in OneLake, and both use Delta-format storage underneath. That shared foundation can make the choice look cosmetic. It is not. Development style, transaction requirements, data shape, governance, team skills, and downstream workloads determine which experience fits better. The decision sits squarely inside the architecture […]

A semantic model is often drawn as a clean layer between data and reports. In production, it becomes a contract: measures define business meaning, relationships define how filters move, storage mode defines how data is reached, security defines who can see what, and downstream reports depend on names and behavior remaining stable. Those responsibilities are […]

Direct Lake changes how Fabric analytics teams think about the boundary between stored data and semantic models. Instead of importing a second copy of Delta data into the semantic model, Direct Lake can load needed columns from OneLake-backed Delta tables as queries require them. For DP-600, that makes storage mode, semantic-model design, and enterprise-scale performance […]