TimeXtender Release Highlights: Xtend26 Edition

The annual TimeXtender Xtend event recently took place at Circuit Zandvoort: alongside a celebration of 20 years of TimeXtender, there was of course also plenty of room for the latest features and development direction of TimeXtender. New releases of TDI and TDP came out alongside Xtend as well; below are the most notable updates.

Data source providers

The main improvement here is the addition of streaming support for large file-based data sources such as CSV, Parquet, and XML/JSON. Previously, TimeXtender Ingest Services had to load the entire source file into memory and convert it to parquet before the data was sent to Ingest storage. This required quite a lot of memory on the TIS VM. A common workaround was to split files into chunks externally, or to limit ingestion to the minimum. With the streaming solution, fewer resources are needed and the data can start flowing sooner. The speed gain is quite dramatic for large files: for a 4GB CSV file, we saw an improvement from a 7-hour load to 25 minutes (with specific infrastructure and scale), along with much lower RAM and CPU usage. The Parquet and XML/JSON providers now also support incremental load in TDI.

There are also further improvements and fixes for many connectors; you can find the details here: Data Source Providers r. 2026-05-13 | Community

TDI Preview Features

As of the TDI 7361.1 release, there are a number of Public Preview features: these are ready to use, but stability improvements are still being worked on. Alongside Fabric Lakehouse and Fabric SQL for Prepare storage, you can now also use Fabric Warehouse as Prepare storage in Simple Mode. If you're planning to switch to this Fabric variant, you could already test it in a Prepare-to-Prepare setup. This does require a Lakehouse Ingest, and a Lakehouse Prepare for transformations is then recommended.

Fabric Lakehouse now supports Materialized Views, allowing you to use these with Table Inserts. This avoids the drawbacks of the SQL Analytics endpoint involved with regular views.

For Snowflake, we now have support for Snowflake on AWS: you're no longer limited to just the Azure variant. In addition, you can now also define table-level security in your Snowflake Prepare using the same functionality we already know from SQL Server. However, since Snowflake doesn't implement DENY the way SQL Server does, you can only define positive authorizations.

The MCP Server has now reached version 2.0. This means support for https connections, Snowflake, Fabric, and a direct cloud connection that lets you expose your MCP Server to AI tools hosted elsewhere without having to set up firewalling. You can make multiple models available through a single MCP server, instead of the limitations that existed before.

For Ingest metadata, you can now define data type overrides directly from the editing screen in the Metadata Manager, instead of constantly switching back and forth between the Metadata Manager and the Data Type Override menu. Import/export options have also been added that make it a lot faster to set up sources that resemble each other.

There are also many smaller improvements and fixes; check out the full release notes here: TimeXtender Data Integration 7361.1 (Public Preview) | Community

TimeXtender Data Platform 26.2

During Xtend, there was a showcase of the TimeXtender Data Platform: the main starting point for TDI / Orchestration / Quality. Instead of separate portals for all the components, this will soon become the central way to manage every aspect of TimeXtender. Starting with 26.2, you'll see a new layout and new features, which will be gradually rolled out over the coming period. This also comes with an API that lets you manage API keys and orchestration programmatically. From now on, you can control and configure Data Quality, Data Enrichment, and Orchestration right in the browser to give your data the quality needed for applying AI. You can also do the latter from TDP in the Public Preview of Xpilot Analytics. Xpilot Analytics uses your MCP Server to retrieve insights from your data through a dialogue. The entire chain of TimeXtender solutions comes together this way to enrich your data into a set that's ready for AI.

Check out the full list of changes here: TimeXtender Data Platform 26.2 | Community

All in all, there's a lot to check out! This summer we're visiting a lot of customers on our TimeXtender Summer Tour to demonstrate the TimeXtender Data Platform in full, and we're also offering an online demo of TimeXtender if we've sparked your interest.