2026-W37

Sep 6, 2026 – Sep 13, 2026 · 9 articles · 0 low-priority · latest →

API Automation · 1

Lineage-aware AI with the Fabric item relations API (Preview)

Fabric Updates Sep 10, 2026 4/5 · Tier A API AutomationGovernanceAI Readiness deep-dive

<p>The <strong>Fabric item relations API</strong> exposes upstream and downstream lineage through two preview REST operations. Responses include related items, typed dependency edges, and workspace metadata, allowing applications to resolve dependencies across workspace boundaries.</p><p>Calls require <code>beta=true</code> and Read permission on the queried item; both users and service principals are supported. Relation types include Association, Shortcut, PushData, Orchestration, Datasource, and parent-child or hidden dependencies. The API is preview-only and not recommended for production use, so consumers must tolerate contract changes.</p>

Preview REST operations expose typed Fabric lineage for impact analysis, CI/CD checks, catalogs, and grounded AI agents.

Community · 1

Fabric Influencers Spotlight: August 2026

Fabric Updates Sep 8, 2026 2/5 · Tier B CommunityGovernance community-event

<p>The August Fabric Influencers Spotlight aggregates 42 community submissions across governance, engineering, warehousing, AI, Real-Time Intelligence, semantic models, and certifications. Relevant entries include cross-tenant private endpoints, RLS and Purview labeling, auditing and DLP, Fabric CI/CD decisions, CU-cost measurement, ADF migration, pipeline automation, and Delta Lake.</p><p>It is a discovery index rather than a technical analysis: the extract gives short descriptions but not the configurations, measurements, limitations, or evidence needed to make an architecture decision.</p>

Community roundup surfaces relevant governance, CI/CD, capacity, migration, and engineering links but provides little original technical detail.

Direct Lake · 1

Choosing A Power BI Storage Mode For Reporting On Data From Azure Databricks

Crossjoin Sep 6, 2026 4/5 · Tier A Direct LakeSemantic Model deep-dive

<p>A new comparative paper evaluates Power BI storage choices for semantic models over Azure Databricks data. The results are not universal: <strong>Direct Lake on OneLake</strong> performs best in most tested scenarios, while a composite model with a DirectQuery fact table, Dual dimensions, and Import aggregations is stronger for larger data volumes.</p><p>The analysis focuses on performance and does not evaluate cost or governance. Its practical value is the decision framework: reproduce representative tests against your own data, model shape, report queries, and volume before selecting a storage mode.</p>

Comparative testing identifies when Direct Lake wins and when composite storage modes better handle larger Databricks volumes.

Governance · 1

Get ready for table discovery in OneLake Catalog search (Preview)

Fabric Updates Sep 9, 2026 4/5 · Tier A GovernanceAPI AutomationAI Readiness announcement

<p><strong>OneLake Catalog</strong> will return tables as standalone search results, starting in late September. Users can search by table name, description, or exact column name across semantic models, lakehouses, and mirrored databases without knowing the parent workspace.</p><p>Discovery requires <strong>Read</strong> permission on the parent item, but not Read Data or Read All. It does not grant data access, and tables protected by object-level security remain hidden. Administrators can disable the <strong>Users can find objects in search</strong> tenant setting to keep results at item level. The same permission-aware search is exposed through the Search API and Fabric MCP surfaces.</p>

Preview table discovery changes metadata visibility, tenant governance, API automation, and permission expectations.

Real-Time Intelligence · 1

Designing scalable Business Events in Microsoft Fabric

Fabric Updates Sep 10, 2026 3/5 · Tier B Real-Time IntelligenceData Quality deep-dive

<p>Business Events should be treated as durable product contracts rather than one-off triggers. Events describe factual business occurrences, use names such as <code>Retail.Inventory.LowStockThreshold</code>, and leave reactions to independently owned consumers.</p><p>The guidance recommends explicit <strong>dataVersion</strong> values, parallel operation during breaking schema migrations, and idempotent consumers because delivery retries can continue for up to 24 hours. Existing subscriptions remain on their selected version rather than upgrading automatically. Event chains should represent meaningful business stages; centrally controlled, multi-step workflows belong in orchestration instead.</p>

Practical Business Events guidance addresses fanout, schema evolution, retries, idempotency, and event-chain design.

Semantic Model · 2

Find What Changed And When In Your Power BI Semantic Model

Crossjoin Sep 13, 2026 4/5 · Tier A Semantic Model tutorial

<p><strong>DAX INFO functions</strong> expose metadata such as the last modification time of measures and other semantic-model objects. Querying <code>INFO.MEASURES()</code> in DAX Query View or DAX Studio lets you sort measures by modification time and compare model changes with the start of a performance problem.</p><p>A broader query can combine measures, calculated columns, calculation groups, roles, perspectives, and other objects. Newer metadata functions may fail on older compatibility levels, and adding a calculation group can unexpectedly update many measure timestamps, so the results are useful evidence rather than a perfect audit log.</p>

A concrete DAX metadata technique exposes modification timestamps for diagnosing unexplained semantic-model performance regressions.

Writing DAX at the correct granularity

SQLBI Sep 7, 2026 4/5 · Tier A Semantic Model tutorial

<p>DAX totals can be numerically valid while being semantically wrong when a business rule is evaluated above the grain where its inputs have unambiguous meaning. The article distinguishes the source-table grain, business-rule grain, evaluation grain, and requested output grain.</p><p>For the lemonade example, ingredient rates belong to recipes while water belongs to batches. Iterating batches is correct, but iterating recipes is more efficient: aggregate water by recipe, apply each recipe’s rate, and aggregate only afterward. The resulting totals are 38 sugar spoons and 3.20 liters of lemon juice, rather than values based on unweighted average rates.</p>

The article gives a rigorous grain-based method for preventing plausible but incorrect DAX totals and avoiding unnecessary iteration.

Warehouse · 1

Advancing the Microsoft Fabric SQL Query Editor

Fabric Updates Sep 8, 2026 3/5 · Tier B WarehouseReport Authoring announcement

<p>The Fabric SQL Query Editor now provides a more scalable web development experience for large warehouses. The GA data grid supports column resizing and larger LOB values, while the redesigned Object Explorer, pinned objects, and more responsive IntelliSense improve navigation and authoring across large schemas.</p><p>Query workflows also gain controllable autosave, bulk query management, .sql import and export, and preview sharing that preserves query context and results. GA integrations extend validated SQL work into OneLake analysis and the semantic layer, with a preview path to Fabric Activator operational workflows.</p>

GA improvements make warehouse SQL development scalable, portable, and connected to OneLake, semantic models, and operational workflows.