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A virtual data pipe is a collection of processes that take raw data from various sources, convert it into an format that can be utilized by applications, and store it in a location like a database. The workflow can be programmed to run in accordance with an interval or on demand. It is often complicated with a lot of steps and dependencies. It should be easy to keep track of the relationships between each process to ensure that it’s running as planned.
Once the data has been ingested, it goes through a process of initial cleaning and validation, and may be transformed at this stage through processes like normalization enrichment, aggregation or masking. This is an essential step, as it ensures that only the most accurate and reliable data is utilized for analytics and use.
The data is then consolidated and moved to its final storage place and can be used for analysis. It could be a data warehouse with a structure, such as an data warehouse, or a data lake which is less structured.
To accelerate deployment and increase business intelligence, it’s usually recommended to implement an hybrid architecture in which data is transferred between cloud storage and on-premises. To do this effectively, IBM Virtual Data Pipeline (VDP) is a fantastic choice because it is an efficient, multi-cloud copy management solution that allows application development and test environments 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.