A virtual data pipeline is a set of procedures that take raw data from a variety of sources, transforms it into an actionable format for use by applications and then saves it in a destination system, such as a database or data lake. The workflow can be programmed to run according to the timetable or at any point. As such, it is often complex with many steps and dependencies – ideally it should be able to track each process and its connections to ensure that all operations are running smoothly.

After the data has been ingested it undergoes some initial cleaning and validation, and may be transformed at this stage by processes such as normalization, enrichment, aggregation, filtering or masking. This is an important step as it ensures only the most precise and reliable data will be used in analytics.

The data is then consolidated and moved to its final storage place in order to be used for analysis. This could be a structured database such as a warehouse, or a less structured data lake according to the needs of the organization.

To accelerate deployment and increase business intelligence, it’s usually beneficial to utilize an hybrid architecture in which data is moved between cloud and on-premises storage. To achieve this, IBM Virtual Data Pipeline (VDP) is a fantastic choice as it provides an efficient multi-cloud copy control solution that allows for dataroomsystems.info/data-security-checklist-during-ma-due-diligence/ application development and test environments to be separate from production infrastructure. VDP uses snapshots and changed-block tracking to capture application-consistent copies of data and provides them for developers through a self-service interface.