A virtual data pipeline is a set of processes that collects raw data from sources and converts it into an actionable format to be used by applications. Pipelines can serve a variety of purposes, including analytics, reporting and machine learning. They can be set up to run data according to a schedule or on demand. They can also be utilized for real-time processing.
Data pipelines can be complex with many steps and dependencies. Data generated by a single application can be sent to multiple pipelines, which feed additional applications. The ability to monitor these processes, as well as their connections to each other, is important to ensuring that the entire pipeline is operating correctly.
Data pipelines are utilized in three different ways: to accelerate development, improve business intelligence, and lower risk. In each of these cases the goal is to process a large volume of data and transform it into an actionable form.
A typical data pipeline contains several transformations like filtering and aggregation. Each stage of transformation could require the use of a different data store. Once all the transformations are completed, the data will be moved into the destination database.
To reduce the time it takes to store and transport data Virtualization technology is commonly used. 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 quickly deploy an automated data pipeline to help DevOps operations and https://dataroomsystems.info/should-i-trust-a-secure-online-data-room speed up cloud data analytics and AI/ML efforts. The patented virtual pipe solution from IBM provides an efficient multi-cloud copy management system that separates development and test infrastructure from production environments. IT administrators can create masked copies of on-premises databases via a self-service interface to quickly enable development and testing.