A virtual data pipe is a collection of processes that transform raw data from source systems into an format that can be consumed by software. Pipelines can be used for different reasons, such as reporting, analytics and machine learning. They can be configured to process data on a predefined schedule or at any time, and can also be used to perform real-time processing.
Data pipelines can be a bit complicated with a variety of steps and dependencies. The data generated by a single application can be transferred to multiple pipelines that feed other applications. It is crucial to keep dataroomsystems.info/should-i-trust-a-secure-online-data-room/ track of these processes as well as their relationships to ensure that the pipeline operates properly.
Data pipelines are utilized in three main ways: to accelerate development, enhance business intelligence, and reduce risk. In each the aim is to take a large amount of data and transform it into a form that can be utilized.
A typical data pipeline includes several transformations like filtering and aggregation. Each stage of transformation may require a different data store. Once all of the transformations are finished the data will be pushed into the destination database.
Virtualization is a technique used to reduce the 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 enable DevOps and accelerate cloud data analytics and AI/ML efforts. IBM’s patent-pending virtual data pipeline solution is an integrated copy management system for multiple cloud platforms that allows test and development environments to be separated from production environments. IT administrators can swiftly enable test and development by setting up the databases with masked copies using the self-service GUI.
