What is a Virtual Data Pipeline?

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A virtual data pipeline is a set of procedures that take raw data from different sources, converts it into a usable format for use by applications, and then saves it in a destination system, such as a database or data lake. This workflow can be configured to run according to a timetable or on demand. This is often complex, with lots of steps and dependencies – ideally it should be able to monitor each step and its associated processes to ensure that all operations are running smoothly.

Once the data has been ingested it undergoes some initial cleaning and validation. It may be transformed at this stage by means of normalization, enrichment, aggregation, filtering or masking. This is a crucial step as it ensures only the most accurate and reliable data can be used in analytics.

The data is then consolidated and pushed into its final storage space where it is accessible to analyze. It could be a database that has an organized structure, like an data warehouse, or a data lake that is less structured.

It is often desirable to adopt hybrid architectures, where data is moved from on-premises storage to cloud. To do this effectively, IBM Virtual Data Pipeline (VDP) is a good choice as it provides an efficient multi-cloud copy control solution that permits the development and testing environments of applications to be separate from the 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.

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