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 72 employees internationally. More than 3,300 customers use TimeXtender, which is sold exclusively via a worldwide network of over 200 partners.
TimeXtender is a low-code data estate builder. Using TimeXtender, organizations can easily integrate their siloed data into a data lake, model a data warehouse, and define data marts for multiple BI tools and endpoints. TimeXtender overlays the data storage infrastructure, connects to any data source, and integrates data preparation capabilities that users need into a single, unified solution.
TimeXtender’s main goal is to empower 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 and more recently with Snowflake. 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, Azure SQL Database and Snowflake 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 eliminated. Predefined sets of tasks (known as ‘Execution Packages’) also 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 provides hotfixes in between for critical issues. Upcoming development steps include extending connectivity and adapting the functionality of the deployment model.
In the future, TimeXtender plans to further expand the number of target systems it supports. Snowflake support was just the first step in moving beyond existing Microsoft target systems to offer greater choice to customers.
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, 77 percent for data integration and 67 percent for data warehouse automation. Interestingly, 53 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 30 percent for this task. But this number has increased by 14 percentage points since last year. Perhaps it takes a little time for customers to understand how to utilize TimeXtender to maintain data lakes next to data warehouse environments.
Among its customer base of mainly mid-sized companies of 101-2,500 employees, a median of 3 and a mean of 35 users is quite a low rate of penetration. Furthermore, only 23 percent of respondents are using the tool for self-service analytics. These numbers suggest that the tool is mainly used by technical experts rather than by business users.
Surprisingly, more than half of the users surveyed claim to be using TimeXtender in company-wide scenarios or in several divisions. This could lead to the conclusion that TimeXtender can also serve large scenarios with little human effort.
Use cases
n=30
Extend of usage in the company
n=18
Total number of users per company
n=30
Total number of developers per company
n=19
Company size (number of employees)
n=30
Summary of TimeXtender highlights
User ratings for TimeXtender are consistently above average except in the Performance, Innovation and Automation KPIs. However, 32 percent of respondents claim to experience no significant problems at all with TimeXtender. ‘Automation of data processes’ and ‘ease of use for technical users’ are each highlighted by an impressive 84 percent of respondents as significant reasons why they chose TimeXtender. Its ‘connectivity to source and target systems’ (68 percent) and ‘integrated end-to-end platform’ (53 percent) also appeal to potential buyers. TimeXtender achieved a Net Promoter Score of 53 and an excellent set of results including three top ranks and eight leading positions in the Products to Support DW Automation peer group in this year’s Data Management Survey.
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