What is a Virtual Data Pipeline?

A virtual data pipeline is a set of processes that collects raw data from source systems and converts it into a format that is able for use by applications. Pipelines can be used for many purposes, including reporting, analytics and machine learning. They can be configured to process data on an established schedule or on demand, and may also be used for real time processing.

Data pipelines are typically complicated, requiring many steps and dependencies. Data generated by one application can be transferred to multiple pipelines that feed other applications. It is vital to be able to monitor these processes as well as their relationships to ensure that the pipeline functions correctly.

Data pipelines are utilized in three ways: to speed up development, improve business intelligence, and reduce risk. In each the aim is to take a large amount of data and turn it into a form that can be used.

A typical data pipeline contains the series of transformations, including filtering and aggregation. Each stage of transformation might require a different data store. When all the transformations are completed the data is then pushed into the destination database.

Virtualization can be used to cut down the time needed to collect and transfer data. This allows the use of snapshots and changed-block tracking to capture application-consistent copies of data in a much faster https://dataroomsystems.info/should-i-trust-a-secure-online-data-room way than traditional methods.

IBM Cloud Pak for Data powered by Actifio enables the deployment of a virtual data pipe quickly and easily. This will allow DevOps, and accelerate cloud data analysis and AI/ML efforts. IBM’s patented virtual data pipeline solution is a multi-cloud copy control system that allows test and development environments to be decoupled from production environments. IT administrators can easily enable development and testing by provisioning the databases with masked copies using a self-service GUI.