A virtual data pipe is a collection of processes that convert raw data from source systems into an format that can be consumed by software. Pipelines are beneficial for a variety of purposes, such as analytics, reporting, and machine learning. They can be configured to process data according to a predefined schedule or on demand, and can be used for real-time processing.

Data pipelines can be complex, with numerous steps and dependencies. For example the data produced by one application can be fed into multiple other pipelines, and then feed into different applications. The ability to track these processes, as well as their connections to each other is click for source essential to ensure that the pipeline runs properly.

There are three primary use instances for data pipelines – increasing development speed, enhancing business intelligence and mitigating risk. In each case the aim is to gather a large amount of data and turn it into a form that can be utilized.

A typical data pipeline consists of a series transformations such as filtering and aggregation. Each stage of transformation may require a different type of data store. After all transformations have been completed and the data has been pushed into the destination database.

To cut down on the time needed to transfer and capture data Virtualization technology is often utilized. 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.

IBM Cloud Pak for Data powered by Actifio allows you to deploy a virtual data pipe quickly and easily. This will enable DevOps and speed up cloud data analysis as well as AI/ML initiatives. The patented virtual data pipe solution from IBM offers a reliable multi-cloud copy management system that decouples test and development infrastructure from production environments. IT administrators can quickly enable development and test by provisioning masked copies on-premises databases through an easy-to-use GUI.

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