TimeXtender is a software provider specializing in data warehouse automation. Its headquarters are located in Aarhus (Denmark) and Washington (USA). Having identified the need for an automated solution to extract data with the least possible hand-coding and one which reduces or eliminates repetitive tasks, TimeXtender was founded in 2006. In the following years, the company embarked on technology partnerships with several large companies and increased its activities in the United States. The company now has 50 employees internationally. More than 3,300 customers use TimeXtender, which is sold exclusively via a worldwide network of 200 partners.

The software – also called TimeXtender (current release: 20.10.21) – is a data management platform that automates, orchestrates and documents the modern data estate. It is designed to help organizations to build and maintain complete data estates, including data lakes, data warehouses and data marts/sematic models. TimeXtender’s main goal is to engage non-technical users by automatically generating code to extract data. However, it also supports custom code if necessary. In 2017, TimeXtender moved to a subscription-only model, making it a software as a service (SaaS) company.

TimeXtender consists of TimeXtender Portal, TimeXtender ODX Service and TimeXtender Desktop. It supports on-premises, cloud and hybrid deployment models. For cloud, it is closely associated with Microsoft Azure. Currently, implementations are mostly deployed on-premises. Its data model is highly configurable and supports most modeling styles, in particular Inmon and Kimball. To extract data, TimeXtender supports over 230 different data sources, the most common of which are SQL Server, SAP, Dynamics, Oracle, DB2, REST and CSV.

The user is guided step-by-step from data ingestion and building a data warehouse model to building data marts. TimeXtender views the data warehouse as a whole, rather than as single pipelines.

Structures and relationships are arranged along the process on one screen. This provides a clear overview and reduces errors. TimeXtender emphasizes simplicity and brings with it a no-code/low-code development interface. The developed models can then be deployed to the appropriate environment at the push of a button. Microsoft SQL Server, Azure Synapse Analytics and Azure SQL Database are currently supported as target environments. All developments are stored in the form of metadata. This not only provides the basis for the openness of the system and exchangeability of content, it also forms the basis for further development support functions. For example, TimeXtender uses metadata-based data lineage to detect data dependencies and automatically orchestrate entire projects based on this information.

For development efficiency, this could be very useful as the step of human data review could be reduced or, in TimeXtender’s opinion, even removed. Also, predefined sets of tasks (known as ‘Execution Packages’) help to simplify development and can accelerate time to market. For performance, TimeXtender reorganizes the execution plan to provide the fastest possible execution of all selected tables. To improve reliability, errors generated by the software or by underlying platforms can be logged and analyzed from the point of failure. Error notifications are sent by e-mail and rollbacks are also possible. TimeXtender follows a 3-week release cycle and provide hotfixes in between for critical issues. Upcoming development steps include extending connectivity and adapting the functionality of the deployment model.

User & Use Cases

The tool has a clear focus on data warehousing, which is borne out in the numbers: 100 percent are using TimeXtender for data warehousing/BI, 81 percent for data warehouse automation and 71 percent for data integration. Interestingly, 55 percent confirm that the tool is also used for data preparation by business users. This seems to confirm the vendor’s approach of automating processes and reducing the need for hand-coding to attract less technical users.

Although it is marketed as a tool to build data lakes, it is only used by 16 percent for this task. The mean of 58 users per company indicates that there are plenty of experts with skills in SQL and data modeling using the tool. However, the median of 4 users shows that only a small number of users work with the tool in most cases and also that very few companies use it extensively. TimeXtender is mainly used in midsize businesses, but also in some small and large companies.

Current use


Total number of users per company


Total number of administrators per company


Company size (number of employees)


Summary of TimeXtender highlights

TimeXtender achieved consistently good ratings with two top rankings and three leading positions in this year’s Data Management Survey. It was placed above average in 14 of the 17 KPIs, including one maximum rating of 10/10 for Connectivity and a 9.3/10 in the Technical Capability KPI. 37 percent of respondents claim to experience no significant problems with TimeXtender. Impressively, the lowest placings for TimeXtender were two fourth positions in the Performance and Customer Experience KPIs.

Technical Capability – Top-ranked

Peer group: Products to Support DW Automation

Connectivity – Top-ranked

Peer group: Products to Support DW Automation

Recommendation – Leader

Peer group: Products to Support DW Automation

Platform Reliability – Leader

Peer group: Products to Support DW Automation

Functionality – Leader

Peer group: Products to Support DW Automation

Want to see the whole picture?

BARC’s Vendor Performance Summary contains an overview of The Data Management Survey results based on feedback from TimeXtender users, accompanied by expert analyst commentary.

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Peer Groups Products to Support DW Automation
Number of responses31
OfficesHQs in Aarhus (DK) and Washington (USA)
Customers>3300 (2021)
Revenues (2020)Not disclosed

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Ease of use and the flexibility to build DWH solutions very fast. It is both easy to learn but also has the flexibility to include complex queries and data structures if needed.
Person responsible/project manager for BI from IT department, retail/wholesale/trade, 100-2,500 employees
Very good performance in loads, generally no limit to what can be done, easy to see where data comes from and transformations done.
Person responsible/project manager for BI from IT department, services, 100-2,500 employees
Great tool to implement a datawarehouse, in which all the necessary processes can be handled in one tool.
Data engineer/data manager, technology sector, 100-2,500 employees
A fantastic tool that can help a lot of companies get control of their data and provide business users with a lot of value, fast!
Data engineer/data manager, IT, less than 100 employees
Best DWH builder I have worked with.
Person responsible/project manager for BI from IT department, manufacturing, 100-2,500 employees