A virtual data pipe is a set of processes that extract raw data from different sources, convert it into a format that can be utilized by applications, and then store it in a destination like databases. This workflow is able to be configured to run in accordance with a timetable or upon demand. It is usually complex, with many steps and dependencies. It should be easy to monitor the connections between the various processes to ensure that everything is running smoothly.

Once the data has been ingested it is subjected to a preliminary cleansing and validation. The data can be transformed at this stage by processes such as normalization, enrichment, aggregation, filtering or masking. This is an essential step, since it guarantees that only the most reliable and accurate data is utilized for analytics and usage.

Then, the data is stored in a consolidated form and is then transferred to its final storage space where it can be easily accessed to analyze. This could be a structured database, such as a warehouse or a less structured data lake depending on the needs of the company.

To accelerate deployment and enhance business intelligence, it is often desirable to use an architecture that is hybrid, where data is moved between on-premises and cloud storage. IBM Virtual Data Pipeline is an excellent choice to achieve this, as it offers an option for multi-cloud copies that allows development and testing environments to be decoupled. VDP uses https://dataroomsystems.info/how-can-virtual-data-rooms-help-during-an-ipo snapshots and changed-block tracking to capture application-consistent copies of data and provides them for developers through a self-service interface.