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. Review collected by and hosted on G2.com.

Documentation and learning curve: several users describe TimeXtender's documentation and knowledge base as unstructured and hard to search, and note that without completing the vendor's formal training, getting comfortable with concepts like RSD-files and more advanced transformations takes real time. This makes onboarding new team members slower than it needs to be.

Multi-developer collaboration and deployment: teams working on the same project report friction with version control and "code promotion" — everything in a development environment tends to need to move to production together, so you can't easily cherry-pick just the changes you want to release. Synchronization issues also crop up when multiple developers work in a project at the same time.

Coding assistance is thin: the built-in SQL editor lacks intellisense/autocomplete, and calling stored procedures or working with more advanced custom SQL scenarios isn't always clearly documented, so power users sometimes fall back to writing raw SQL outside the tool.

Error handling and stability on large datasets: some users note inconsistent or unclear error reporting when working with large data volumes, plus occasional reliability issues with features like external executables, which can make troubleshooting slower than expected.

Pace of feature rollout and roadmap visibility: a few of the newest capabilities (like full Microsoft Fabric integration and the new AI-driven "Xpilot Analytics" feature) are still in public/private preview rather than fully generally available, and some users would like clearer visibility into what's coming next and when. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Troubleshooting could still be improved, particularly for complex deployment failures and dependency-related issues. Clearer error messages that identify the root cause and suggest possible solutions would make problem resolution faster. Some areas of the user interface could also be made more intuitive and consistent. Review collected by and hosted on G2.com.

TimeXtender provides a solid platform to automate data extraction, transformation, and loading (ETL) processes. It works well with popular data warehouses like Azure Synapse, Snowflake, and SQL Server, making it easier to manage data across different systems. The tool’s metadata system helps track changes and manage data versions, which keeps data consistent and reliable. I also appreciate how it handles incremental data loads and runs processes in parallel, which speeds up data refresh times and improves performance. Review collected by and hosted on G2.com.

TimeXtender can use a lot of memory and processing power, especially with large data sets, which sometimes slows down performance on less powerful machines. The user interface is practical but could be improved with better options to view and manage complex data flows, which would help when troubleshooting. Also, since it relies on network access, there’s no way to work offline or without an internet connection. Lastly, it takes some time and effort to learn how to use all the automation and metadata features effectively. Review collected by and hosted on G2.com.

TimeXtender is one of the most effective tools I’ve worked with, and a big reason is how strongly it prioritizes user enablement through its training academy.

The platform includes structured, in-depth training at no cost, which gives users the foundation they need to confidently integrate databases, configure the system, and manage deployment. That kind of proactive support removes a lot of friction during implementation and helps drive better overall efficiency.

Being able to prepare thoroughly before you even start using the product makes adoption feel much smoother. Overall, the level of support—and the clear commitment to helping users succeed—is genuinely commendable and very much appreciated. Review collected by and hosted on G2.com.

Without completing the recommended training, it's deployment process can feel fairly complex and hard to navigate, and it isn’t always clear what the next step should be. Review collected by and hosted on G2.com.

The features are enhanced now and are ready to compete with any competitor. Review collected by and hosted on G2.com.

No disliktes. Please keep releasing the new KB articles related to Jet Data Integration Review collected by and hosted on G2.com.

Visualizations are still excellent. There seems to be better integration features. Review collected by and hosted on G2.com.

Still wish more online trainings! But processes are better to integrate. Review collected by and hosted on G2.com.

In my opinion, one of the main advantages of TimeXtender is that, when used correctly, it encourages you to focus on designing a solid semantic model rather than getting bogged down in code optimization. Incremental loading becomes a straightforward process, accomplished with just a few clicks, rather than having to build it from the ground up or risk inconsistent methods among different developers.

As you become more familiar with how TimeXtender works, you may find that you can replace or simplify views by using lookups and adopting a table-to-table approach. If you notice that you’re repeatedly applying the same transformations or building views in a similar way, it’s a good idea to turn that process into a snippet.

Finally, if you’re dealing with multiple data connections that share the same configuration, you can streamline your workflow by adding an additional source to your main source. This allows you to manage transformations or changes across all sources at once, reducing the effort required to update each one individually. Review collected by and hosted on G2.com.

As an organization currently managing over 300 data sources across two production environments, we are increasingly feeling the pressure to stay ahead of any changes in our data. Over time, we anticipate reaching a point where we must choose between finding a way to maintain fast data loads that continue to meet our customers' expectations, or considering moving away from bundling the data altogether and looking into alternative products. Review collected by and hosted on G2.com.

The platform makes it easy to maintain consistent, high-quality data across systems, and its automation features save significant time compared to manual data engineering. The lineage and documentation are clear, making governance much easier. Overall, it helps us ensure trust in our data while reducing operational overhead. Review collected by and hosted on G2.com.

One drawback is that maintaining larger projects can become complex, especially when many teams collaborate. Versioning, governance, and dependency management could be more streamlined to reduce administrative overhead. Review collected by and hosted on G2.com.

I like its ability to update and sync all elements in real time across all your devices and third-party applications. Review collected by and hosted on G2.com.

I haven’t had any issues with this tool. Review collected by and hosted on G2.com.

Clear response to incidents, specialized support that will never leave you with doubts. Review collected by and hosted on G2.com.

Perhaps the response time is good but not instant. Review collected by and hosted on G2.com.