AARHUS, Denmark & BELLEVUE, Wash. – September 2018 — In today’s business world, data is being generated at an ever-increasing rate. Some tech analysts have estimated that the amount of data doubles every two years.
In an effort to solve this challenge that organizations face of how to manage all their growing data, many have turned to a low-cost, high-capacity Hadoop-based data lake. However, a data lake without a data stream can quickly become a pool of stagnant data, and maintaining a constant flow in and out of the data lake requires a diverse set of very expensive skills.
To address this challenge, TimeXtender, a recognized global software company enabling instant access to any type of data in the organization to support advanced analytics and artificial intelligence (AI), has announced it has developed a new ODX (Operational Data Exchange) and is releasing an upgraded version on September 26 of its Discovery Hub®, of which the ODX is a central component.
Discovery Hub®, with its new ODX, enables users to fully automate this process so that a continuous flow of data can occur in and out of an Azure Data Lake. This function permits organizations to populate the Azure Data Lake with virtually any number and any type of data sources available on the market.
This also allows users to choose Azure Data Lake and all variants of Microsoft SQL server (Azure SQL Database Managed Instance, SQL Database, et al.) as the storage. And, as Microsoft releases new storage options, Discovery Hub® will support those as well.
With this new Discovery Hub® announcement, data will be available in the same structure and quality as in the source systems, but will now be accessible from one location. This takes out the pain of data preparation while also avoiding performance and security issues on source systems. In addition, companies will have the capability to connect AI, predictive or self-service analytics tools; enabling IT to use Discovery Hub® as a single platform for offloading all their data for business users to access and leverage.
The new ODX is built for simplicity and automation, as evidenced by the setup that takes as little as two minutes to complete. This is achieved due to TimeXtender’s proprietary automation engine doing the bulk of the work.
The consolidated data from a company’s data sources will be available from one place in raw data format. Users can choose to connect any analytics tool to the ODX to analyze the raw data, such as AI or predictive for data scientists, or they can choose to further build out their data estate/analytics platform. Because Discovery Hub® automatically tracks all metadata, users will be able to easily retrieve the desired data out of the Azure Data Lake and deliver it into a structured format (Modern Data Warehouse, Shared Semantic Layer). This allows for easy connection to any analytics tool for any department needing more structured and refined data.
The new release entirely supports the current version of Azure SQL Database Managed Instance that represents fully managed SQL Server Instance hosted in Azure cloud — allowing customers to easily take advantage of new technology when released by Microsoft.
“By providing support for Azure Data Lake, TimeXtender’s Discovery Hub® essentially delivers companies, their business users, data scientists, and IT personnel, with a powerful ‘one-stop shop’ for all its data,” said Heine Krog Iversen, co-founder and CEO of TimeXtender. “This capability is vital as companies often struggle to manage all their existing and future data. With Discovery Hub®, organizations can effortlessly manage all their data in one central location, allowing them to more easily capitalize on all its inherent knowledge.”
Along with the support of Azure Data Lake, this new release also includes many new features and improvements to further automate and simplify data preparation and data intelligence from automated conditional lookups, that now perform 30% faster. This upgrade also includes enhancements of the endpoints in the shared semantic layer, making it even easier (and faster) to use the same governed data models enterprise-wide regardless if users choose to work with Power BI, Qlik or Tableau.