Visualizations are still excellent. There seems to be better integration features.

I have continued to appreciate how TimeXtender simplifies the development and maintenance of data warehouse solutions. The automation features save significant development time, while the structured approach makes solutions easier to understand, deploy, and maintain. I also value the continued improvements to the platform and the availability of helpful documentation and support.

Microsoft Fabric support has matured significantly: TimeXtender can now migrate existing solutions directly into Fabric Lakehouse without a rebuild, automatically generating the Spark notebooks from your existing visual, no-code data models (including things like Type 2 slowly changing dimensions). It also added Power BI Direct Lake Mode, so reports read straight from Delta tables for near real-time analytics without duplicating data or waiting on scheduled refreshes. Big performance gains on file loading: the file-loading engine was rebuilt on streaming technology, so large CSV files that used to need ~20GB of RAM now process in about 25 minutes using roughly 100MB. Incremental loading was also extended to Parquet, XML, and JSON files, which cuts down repeated full loads. New AI capabilities are rolling out: an MCP Server 2.0 lets tools like ChatGPT query your governed semantic models (with OAuth auth and support for Snowflake and Fabric alongside Azure SQL), and there's a newer "Xpilot Analytics" feature (currently in private preview) that lets users ask plain-language questions against governed semantic models rather than raw schemas, returning answers with visualizations and full SQL transparency. The whole suite got consolidated into one modern, web-based platform: Data Integration, Data Quality, Data Enrichment, and Orchestration now live under a unified experience with dark mode, a redesigned navigation/portal, and Entra ID group support for access control — a real step up in day-to-day usability. Data quality monitoring improved too: there are now dataset health scores that show quality trends across execution cycles, plus webhook notifications (including a Zapier integration) so quality exceptions can flow straight into tools like Jira instead of requiring someone to check a dashboard.