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A virtual data pipe is an array of processes that convert raw data from sources into an format that can be used by applications. Pipelines can serve a variety of reasons, such as reporting, analytics and machine learning. They can be configured to run data according to a schedule or on demand. They can also be used in real-time processing.
Data pipelines are usually complex, with many steps and dependencies. For example the data generated by one application can be fed to multiple pipelines, which feed into different applications. It is crucial to be able to monitor the processes and their interactions to ensure that the pipeline functions correctly.
Data pipelines are employed in three ways: to speed development, enhance business intelligence, and lower risk. In each of these cases, it is the goal to gather a large amount of data and then transform it into a format that can be used.
A typical data pipeline would comprise various transformations such as reduction, aggregation, and filtering. Each stage of transformation may require a different data store. Once all of the transformations are finished the data will be pushed into its destination database.
Virtualization is a technique used to reduce the amount of time needed to capture and transfer data. This allows the use of snapshots and changed-block tracking to capture application-consistent copies of data in a much faster way than traditional methods.
With IBM Cloud Pak for Data powered by Actifio you can easily set up a virtual data pipeline to enhance DevOps capabilities and accelerate cloud data analytics as well as AI/ML initiatives. IBM’s patent-pending virtual data pipeline solution offers a multi-cloud copy control system that allows test and development environments to be decoupled from production environments. IT administrators can create masking copies of databases on-premises via a self-service interface to quickly enable development and testing.