> For the complete documentation index, see [llms.txt](https://docs.datalogz.io/guides/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.datalogz.io/guides/release-notes/2026.07.0.md). # 2026.07.0 Release date: July 6, 2026 This release is about knowing what your AI will inherit. The new AI Readiness Assessment scores your Power BI semantic models and shows model drift as it happens, exports finally cover the full estate instead of 500 rows, and alerts turn into routed remediation without leaving the page. This release helps your teams to: * Know how AI-ready their Power BI semantic models are, and catch model drift before it becomes a trust problem * Export the full analytics estate in one pass instead of 500 rows at a time * Turn any alert into a routed, tracked remediation workflow on the spot ### What's New #### **AI Readiness Assessment** Before you point AI at your analytics, you need to know what it will inherit. The [AI Readiness Assessment](#ai-readiness-assessment) scores how ready your team's Power BI semantic models are for AI consumption. * A readiness summary page that scores your Power BI semantic models for AI consumption * Findings surface as a dedicated AI Readiness alert category, routed like any other alert * A Recently Changed Models card that shows exactly what changed in each model, with renames shown as old to new names rather than an add plus a remove * Field-level change detail: the specific tables, columns, and measures added, removed, or modified between snapshots **Result:** You see model drift the moment it happens, before it reaches an AI-generated answer. #### **Full CSV exports** Inventory exports are no longer capped at 500 rows. * Exports now stream up to one million rows * Live progress while the export runs, with the option to cancel mid-download **Result:** Your full estate lands in a spreadsheet in one pass, not 500 rows at a time. #### **Run workflows straight from alerts** Remediation now starts where the finding lives. * Trigger workflows directly from an alert's side panel or from an asset's inventory panel * Run them per alert, or in bulk across an asset's active alerts **Result:** Every finding turns into a routed, tracked action without leaving the page. ### Enhancements #### **Smarter monitors** * A new Migration monitor type shows correctly in the Monitor Catalog * Alert categories that previously fell into "Other" now bucket correctly * Redesigned alert emails: a clear per-alert card with key details, a "First seen" timestamp, and a direct "View alert" button * Better saved views: rename and re-share your saved Inventory views in place, set sort explicitly per column, and keep sort and filter state across reloads and shared links * Accessibility and visual refresh: a broad WCAG cleanup plus a calmer, more readable design across tables, the Home dashboard, navigation, and tooltips * Alerts can now be sorted by first-triggered and closed dates. #### Platform Stability & Performance * Power BI syncs now capture only what changed since the last run, cutting multi-hour syncs and reducing storage footprint by more than 90% in large environments, while keeping deletions accurately reflected. * Tableau metadata extraction self-tunes its paging on very large sites instead of failing, and several classes of intermittent sync errors have been eliminated. #### Bug Fixes * The Power BI Capacities tab now shows only your team's capacities, and the header count matches the tab. * Cloning a monitor and viewing "My Monitors" no longer error. * Alert dialogs opened from shared deep links now load the full asset header instead of a blank title. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.datalogz.io/guides/release-notes/2026.07.0.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.