TimeXtender Data Integration Winter 2026 Release

TimeXtender has released a new version of its products with a wintery theme: major Snowflake improvements. There is also a big leap forward with the TimeXtender MCP Server and Deliver endpoints. In this blog I highlight seven of the key features. For the full details of the TDI and connector releases, see the following links:

• TDI 7256.1 Release details
• Data Source Providers release

Snowflake

Until now, when using Snowflake as the engine for the Prepare instance, you had to set up Ingest storage via ADLS. As of this release, you can also use Snowflake native staging and store raw data in Snowflake. This reduces infrastructure complexity, especially for environments with restrictive security requirements, and shrinks the set of tools you need. Our initial tests show good performance characteristics, and the Data Source Providers have been updated to be compatible with Snowflake ingest storage.

Up to this release, TDI offered a reduced set of ETL features for Snowflake Prepare. With this release, almost all of the missing features become available. It is now also possible to use Add Related Records, Integrate Existing Objects, and pre- and post-scripts with stored procedures. With the latter, you can also address engines other than Snowflake SQL, such as Python, without additional configuration. With a few adjustments in Snowflake, you can also integrate Java, JavaScript, and Scala into your solution. The only feature still to come is Object Level Security and its derived options.

With these extended capabilities, switching from SQL Server to Snowflake (and vice versa) is as easy as it can be. A few subtleties remain around the differences between Snowflake SQL and T-SQL and between data types, but it adds a lot of extra flexibility.

MCP Server

If you have been exposed to AI news, you will have seen several MCP server releases go by lately. MCP servers standardize the availability of capabilities that AI models can use. In the context of TimeXtender, you can now make the structure and meaning of Deliver instances (data marts), and the data they contain, accessible to the AI models and agents you want to use in your organization. With the MCP Server, AI can "understand" how your data models are built and can actually retrieve results from them. Because TimeXtender is metadata-driven and that metadata feeds the MCP Server, you have a fantastic breeding ground for better AI outcomes.

The server has an integrated connection with Claude Desktop and also works easily with other models, including on-prem models from, for example, LM Studio. This gives you the freedom to put together an AI solution that fits your organization's policies. Of course, running AI on-prem does require serious hardware and expertise.

Questions covered by your model can be analyzed in depth.

Although this functionality looks very simple, it opens up a world of possibilities: self-service analytics is now within everyone's reach. Our own tests against AdventureWorks data show that the more advanced models can perform in-depth analyses using the data models you bring in yourself. Of course, this does require well-defined models and good data quality. More on that last point below.

It also means that "with great power comes great responsibility": to work with AI in a sustainable way, you need a policy framework and the awareness that you cannot always blindly trust AI. With a well-built data model, complete with the right descriptions, you can get far more value out of the data you already have.

When you ask the question in exactly the right way, you can rely on the result. We will follow up with a more detailed blog on this later.

For more details, see here.

My colleague Louis de Roo will soon be giving a KPI Masterclass (13 February) where you can learn how to define your metrics better. Check out our Academy agenda here.

Prepare-to-Prepare data movement

From now on, TDI lets you move data from one Prepare instance to another. This allows you to build more complex implementations, such as a situation where a group wants to support separate data warehouses for business units, plus an overarching data warehouse that brings everything together. You can also split your solution into smaller chunks to optimize your infrastructure. At the moment, this works for SQL-based instances.

Salesforce Connector

Salesforce can be a difficult product to open up with standard connectors such as the REST connector. There is now a specialized Salesforce connector with extra filters to retrieve only the most commonly used tables, or to adopt all table and column names in their "friendly" variant.

For more details, see here.

Orchestration Execution Error Insights with XPilot

When you reload your data warehouse, you sometimes run into errors. These often come with a big chunk of logging and stack traces. Because it can be tedious to extract the right information from them, Orchestration now has XPilot-enhanced error insights: in short, AI is applied to derive the likely issue and the steps to resolve it, in readable text, from the various logs. This makes it much faster to get to the core of the issue and avoids wild goose chases.

For more details, see here.

Data Quality Rule Generation with XPilot

In Data Quality, you can now have XPilot analyze your datasets to automatically generate data quality rules. You can steer XPilot in more depth and tweak and adjust the generated rules.

For more details, see here.

Qlik Cloud

"But wait, there is more...": Qlik Cloud now has its own Deliver Endpoint, which lets you push your models to Qlik Cloud as apps and reload them. This removes the need for the workarounds that were previously necessary. It works both for a direct connection to your data and via the Qlik Data Gateway. This makes my (former) green heart beat a little faster again...

At the moment, Qlik Cloud Managed Spaces are not yet directly supported; be sure to get in touch with our own Qlik Ambassador Lennaert van den Brink if you want to know more about Qlik.