A virtual data pipeline is a set of procedures that collect raw data from different sources, transforms it into an actionable format for use by applications, and then store it in a destination system such as a database or data lake. This workflow is able to be set according to a set schedule or as needed. It is usually complex and has many steps and dependencies. It should be easy to monitor the connections between each process to ensure that it’s running as planned.

Once the data has been taken in, a few initial cleaning and validating takes place. It could be transformed by using processes such as normalization enrichment aggregation filtering, enrichment aggregation or masking. This is a crucial step since it ensures only the most precise and reliable data will be used for analytics.

Next, the data is consolidated and pushed into its final storage location where it is easily accessible for analysis. It could be a data warehouse with some structure, such as the data warehouse, or a data lake which is not as structured.

To accelerate deployment and enhance business intelligence, it’s often preferable to employ an hybrid architecture in which data is transferred between on-premises and cloud storage. IBM Virtual Data Pipeline is www.dataroomsystems.info the ideal solution for this, since it offers a multi-cloud copy solution that allows application development and testing environments to be separated. VDP uses snapshots and changed-block tracking to capture application-consistent copies of data and provides them for developers through a self-service interface.