Build Data Solutions 10x Faster with TimeXtender

Easily Consolidate Data from Disconnected Sources: The Operational Data Exchange (ODX) allows organizations to ingest and combine raw data from potentially hundreds of sources into one, centralized data lake with minimal effort.

Universal Connectivity: TimeXtender provides a directory of over 250 pre-built, fully-managed data connectors, with additional support for any custom data source.

Automate Ingestion Tasks: The ODX Service allows you to define the scope (which tables) and frequency (the schedule) of data transfers for each of your data sources. By learning from past executions, the ODX Service can then automatically set up and maintain object dependencies, optimize data loading, and orchestrate tasks.

Accelerate Data Transfers with Incremental Load: TimeXtender provides the option to load only the data that is newly created or modified, instead of the entire dataset. Because less data is being loaded, you can significantly reduce processing times and accelerate ingestion, validation, and transformation tasks.

No More Broken Pipelines: TimeXtender provides a more intelligent and automated approach to data pipeline management. Whenever a change in your data sources or systems is made, TimeXtender allows you to instantly propagate those changes across the entire pipeline with just a few clicks - no more manually debugging and fixing broken pipelines.

Turn Raw Data Into a Single Version of Truth: The Modern Data Warehouse (MDW) allows you to select raw data from the ODX, cleanse, validate, and enrich that data, and then define and execute transformations. Once this data preparation process is complete, you can then map your clean, reliable data into dimensional models to create a "single version of truth" for your organization.

Powerful Data Transformations with Minimal Coding: Whether you're simply changing a number from positive to negative, or performing complex calculations using many fields in a table, TimeXtender makes the data transformation process simple and easy. All transformations can be performed inside our low-code user interface, which eliminates the need to write complex code, minimizes the risk of errors, and drastically speeds up the transformation process. These transformations can be made even more powerful when combined with Conditions, Data Selection Rules, and custom code, if needed.

A Modern Approach to Data Modeling: Our Modern Data Warehouse (MDW) model empowers you to build a highly structured and organized repository of reliable data to support business intelligence and analytics use cases. Our MDW model starts with the traditional "star schema", but adds additional tables and fields that provide valuable insights to data consumers. Because of this, our MDW model is easier to understand and use, answers more questions, and is more capable of adapting to change.

Maximize Data Usability with a Shared Semantic Layer: Our MDW model can be used to translate technical data jargon into familiar business terms like “product” or “revenue” to create a shared "semantic layer" (SSL). This SSL provides your entire organization with a simplified, consistent, and business-friendly view of all the data available, which maximizes data discovery and usability, ensures data quality, and aligns technical and non-technical teams around a common data language.

Increase Agility with Data Marts: The SSL allows you to create department or purpose-specific models of your data, often referred to as "data marts". These data marts provide only the most relevant data to each department or business unit, which means they no longer have to waste time sorting through all the reportable data in the data warehouse to find what they need.

Deploy to Your Choice of Visualization Tools: Semantic models can be deployed to your choice of visualization tools (such as PowerBI, Tableau, or Qlik) for fast creation and flexible modification of dashboards and reports. Because semantic models are created inside TimeXtender, they will always provide consistent fields and figures, regardless of which visualization tool you use. This approach drastically improves data governance, quality, and consistency, ensuring all users are consuming a single version of truth.