Table of Contents:
- What is Data Pipeline?
- Why do we use Data Pipeline?
- Advantages of Data Pipeline
Most modern businesses employ a number of platforms to run their day-to-day operations. As a result of advancements in Cloud-based technology, this has occurred.
Companies can now access data on Cloud platforms thanks to Data Pipeline. There is a requirement for a strong method that can automatically aggregate data from multiple sources into a single common destination.
This data can then be analyzed further or transferred to other cloud or on-premise systems.
This article will teach you everything you need to know about Data Pipelines and their advantages.
What is Data Pipeline?
A Data Pipeline is a series of steps that must be completed in a specific order in order to process data and send it from one system to another.
The first stage of a Daat Pipeline is to extract data from the source as input. Each stage’s output is used as the input for the next.
This procedure is repeated until the pipeline has completed its execution. Furthermore, several independent stages may act at the same time under unusual conditions.
A data source, processing stages, and a final destination or sink are usually the three basic components. Users can utilize pipelines to move data from a source to a destination while making changes to it in the process.
Pipelines can have the same source and destination, but they’ll only be used to change data when it’s necessary.
However, the volume, kind, and velocity of data have all changed substantially in recent years, making it more complex.
As a result, pipelines must now be capable of delivering the bulk of enterprises’ Big Data demands. Because the huge volume of data can offer up chances for operations such as Real-time Reporting, Predictive Analytics, and other operations, it is critical for organizations to guarantee that their pipelines do not lose data and can maintain high accuracy.
Pipelines are designed to handle all three characteristics of Big Data: velocity, volume, and variety. Pipelines should be able to manage Streaming Data because of the speed with which data is generated.
The pipeline must process this information in real-time. Because the volume of data generated varies over time, pipelines must be scalable. All sorts of data, including structured, semi-structured, and unstructured data, should be handled via pipelines.
Why Do We Use Data Pipeline?
Data is rapidly growing and will continue to do so. Pipelines are widely used in data ingest and are used to efficiently process all raw data in order to optimize the data created on a daily basis.
Data Analytics, Machine Learning, and applications can all benefit from this altered data. The following are some examples of what a Data Pipeline can be used for:
- To improve customer service, sales and marketing data is delivered to CRM platforms.
- Streaming data from sensors to applications for performance and status monitoring.
- Aggregating together all of the data to accelerate the development of new products.
Advantages of a Data Pipeline
Companies that were unaware of the concept of Data Pipeline used to manage their data in an unstructured and unreliable manner. They did, however, learn about Data Pipeline and how it can help businesses save time and keep data organized at all times. The following are a few of Pipeline’s advantages:
- Replicability of Patterns: It can be reused and repurposed for new data flows. They’re a system of pipelines that fosters a mindset that views individual pipelines as examples of patterns in a larger design.
- Incremental Build: Pipelines allow users to build dataflows one step at a time. Even a little slice of data from the data source might be provided to the consumer.
- Quality of Data: Data flows from source to destination may be easily tracked, accessed, and interpreted by end-users.
This article explained, in detail, what is a Data Pipeline and also discussed its benefits. On the other hand, most modern businesses have a vast amount of data with a dynamic structure.
Companies investigate the need for constructing a Data Pipeline for such data, as well as the complexity of the process because it would demand a significant amount of resources and then ensure that it can keep up with the growing data volume and Schema variations.
For more in depth information on Data Pipelines read this article on Data Pipeline by Hevo.
The author of the article is Anshika Monga