Frequently Asked Questions

Technical Aspects

• How long does it take to implement TimeXtender?

  The time it takes to implement TimeXtender can vary depending on the complexity of the data environment, the size of the organization, and the specific needs of the project. However, TimeXtender’s focus on AI-powered automation rapidly accelerates implementation time.

  Our customers are typically up and running with a fully-functioning data warehouse in a matter of days, rather than months or years.

  Additionally, TimeXtender provides resources such as training, documentation, and support to help ensure a smooth and efficient implementation process.

• How user-friendly is TimeXtender, and what kind of technical expertise is required to use it effectively?

  TimeXtender is designed to be user-friendly with a simple, low-code, drag-and-drop user interface. While some technical expertise is helpful, our tool is designed for those who want to get shit done and does not require extensive knowledge of complex tools and architectures.

  

   

• How does TimeXtender integrate with my existing data infrastructure?

  TimeXtender is a powerful automation layer that’s independent from data sources, storage platforms, and visualization tools. Our technology-agnostic approach ensures that your organization can adapt and grow without being held back by outdated technology or restrictive vendor lock-in.

  TimeXtender integrates with, accelerates, and leverages your existing data infrastructure, and transformation logic is stored as portable SQL code, so you still have a functioning data warehouse if you decide to migrate to a different data integration solution in the future.

• Does TimeXtender fit into the “Modern Data Stack”?

  TimeXtender seamlessly integrates with leading cloud data platforms and visualization tools.

  However, we offer a holistic solution for data integration that eliminates the challenges and limitations associated with the fragmented approach of the conventional “Modern Data Stack”.

  Instead of relying on multiple disconnected tools and systems, we provide a single, low-code user interface for data ingestion, transformation, modeling, and delivery that allows users to build data solutions 10x faster, while reducing costs by 70%-80%.

  Instead of implementing a complex and expensive stack of point solutions, our Unified Metadata Framework provides a solid foundation for AI code generation, data cataloging, data lineage tracking, data quality monitoring, end-to-end orchestration, data governance, and more.

  By focusing on agility and simplifying data integration processes, our holistic solution empowers organizations of all sizes to overcome the challenges and limitations of the fragmented "Modern Data Stack," empowering them to unlock the full potential of their data and drive business value in the fastest, most efficient way possible.

  You can read more about the challenges and limitations of the “Modern Data Stack” here →

• What is TimeXtender’s Unified Metadata Framework?

  TimeXtender's "Unified Metadata Framework" is the unifying force behind our holistic approach to data integration. It stores and maintains metadata for each data asset and object within the platform, serving as a foundational layer for various data integration, management, orchestration, and governance tasks.

  The Unified Metadata Framework enables:

  1. Automatic code generation: Metadata-driven automation reduces manual coding, accelerates development, and minimizes errors.

  2. Data catalog: The framework provides a comprehensive data catalog that makes it easy for users to discover and access data assets.

  3. Data lineage: It tracks the lineage of data assets, helping users understand the origin, transformation, and dependencies of their data.

  4. Data quality monitoring: The metadata framework assists in monitoring data quality, ensuring that the data being used is accurate, complete, and reliable.

  5. End-to-end orchestration: It helps orchestrate workflows and ensures smooth, consistent, and efficient execution of data integration tasks.

  6. Data governance: The framework supports holistic data governance, ensuring that data is collected, analyzed, stored, shared, and used in a consistent, secure, and compliant manner.

  7. And much more!

  Utilizing metadata as our solution's backbone, the Unified Metadata Framework enables a more efficient, agile, and automated approach to data integration, management, orchestration, and governance. This ultimately empowers organizations to build data solutions faster and drive business value more effectively.

• What cloud data platforms does TimeXtender support?

  TimeXtender supports leading cloud platforms including Microsoft Azure, Snowflake, and AWS, with support coming for additional cloud platforms in the near future. TimeXtender offers a seamless experience on all these platforms.

• What business intelligence and analytics tools does TimeXtender support?

  TimeXtender integrates with a wide range of business intelligence and analytics tools, including Microsoft Power BI, Tableau, and Qlik. TimeXtender allows you to easily deploy semantic models to your choice of visualization tool for fast creation and flexible modification of dashboards and reports.

  Additionally, TimeXtender's semantic models provide consistent fields and figures, ensuring all users are consuming a single version of truth, regardless of which visualization tool they are using.

• What types of data sources does TimeXtender support?

  TimeXtender provides a directory of over 250 pre-built, fully-managed data connectors, with additional support for any custom data source, including cloud-based and on-premises data sources.

  TimeXtender can connect to data sources through five types of data providers:

  • Azure Data Factory: TimeXtender’s ADF providers can auto-generate Azure Data Factory pipelines to load data from many common on-premise or cloud based systems making it easily to offload massive amounts of data into a cloud based data estate, while keeping it in sync.

  • Cloud application API Connectors: TimeXtender includes over 150 data providers for connecting to specific SaaS applications such as Salesforce, Google Analytics, Facebook, and YouTube.

  • Generic API & File Connectors: Data stored in files or APIs? No problem. TimeXtender makes accessing this data easier than ever. Connect directly to any REST API with just a few data points. Alternatively, you can connect to JSON, XML, CSV or Excel documents.

  • Cloud or On-Premises Databases: TimeXtender has connectors for nearly any type of database on the planet such as Snowflake, SQL, Synapse, Amazon Redshift, Google BigQuery, Oracle, DB2, Access and more.

  • Bring Your Own Driver (BYOD): Any 3rd party OleDB or ODBC providers installed on the system will be easily selectable in the list of TimeXtender’s supported providers.

  • Custom Data Source: TimeXtender includes many custom developed ADO.net providers making it easier to connect to common systems such as SQL or SAP.

• Does TimeXtender integrate with code repository solutions like GitHub?

  By leveraging our advanced AI capabilities, TimeXtender can automatically generate and deploy all the code necessary to ingest and prepare data, and deploy your projects. TimeXtender can also handle testing and version control natively, eliminating the need for a separate code repository solution like GitHub.

• How developer-friendly is TimeXtender?

  TimeXtender offers a range of tools and features to extend its capabilities. For example, developers can use user-defined functions, stored procedures, reusable snippets, PowerShell script executions, and custom code, if needed. This allows for greater flexibility in data integration workflows and customization to fit specific business needs.

  Additionally, TimeXtender supports version control and allows for separate development and testing environments, making it easy for developers to identify and fix bugs before deploying to production.

  Overall, TimeXtender aims to automate the development, testing, and orchestration of data projects, while still offering developers the tools they need for greater customization and control.

• What are the key differences between traditional data pipelines and TimeXtender's "modern data flows"?

  Traditional data pipelines require a high degree of manual coding and typically involve creating individual, siloed pipelines for each specific dashboard or report. This approach leads to the creation of a massive web of fragile pipelines, each requiring extensive custom coding and individual maintenance, resulting in a lack of cohesion and governance across the data infrastructure. The lack of flexibility in these pipelines makes adapting to new data sources or changing business needs both time-consuming and resource-intensive.

  In contrast, TimeXtender's modern data flows offer a holistic approach to data integration. This method centralizes the flow of data into one streamlined process, enabling the creation of reusable, business-ready data products that can serve a wide range of reporting and analytical needs. This unified approach ensures consistency and governance across all data assets.

  Our data flows are enhanced by low-code simplicity and metadata-driven automation, which significantly reduces manual coding and simplifies the data management process. This not only accelerates the development and deployment of data products but also ensures that they are more adaptable and efficient in meeting the evolving needs of the business.

  You can read more about this topic here.

• How does TimeXtender handle changes in data sources, such as schema or API changes?

  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.

• How does TimeXtender ensure data quality and reliability?

  TimeXtender ensures data quality and accuracy through built-in data profiling and cleansing capabilities that identify and correct errors, missing values, and inconsistencies in data.

  TimeXtender also provides automated data quality monitoring and alerts, enabling users to identify and correct data quality issues before they become problems.

  Advanced data quality capabilities are available through our Exmon Data Governance & Quality product.

• Why doesn’t TimeXtender support real-time data streaming?

  In the batch vs. streaming debate, it’s important to remember that any time data is processed to ensure data quality and reliability, a certain amount of lag time is unavoidable. Because of this, stream processing is only suitable for very simple datasets and use cases, such as streaming website traffic, call volumes, stock prices, etc. There are many simple tools that allow you to connect to these data sources to create real-time analytics dashboards.

  However, batch processing allows for thorough and accurate data cleansing, transformation, and modeling, which makes it the preferred method for more complex and mission-critical analytics workflows. These are the types of workflows TimeXtender focuses on.

  Regardless, the near real-time analytics that TimeXtender’s batch processing provides is more than sufficient to meet an organization’s needs in the vast majority of cases.