To run smoothly their day-to-day operations, modern businesses use many platforms. Cloud-based technologies are responsible for this. Data integration pipelines are a way for companies to connect with Cloud platforms. But what exactly is Data Pipeline, you ask? What does it do?
It is therefore necessary to have a system that automatically consolidates data from many sources into one place. This data can then either be used for further analysis or transferred to other Cloud or On-premise systems.
This article will provide a detailed understanding of the Data Pipeline and its key components. It also explains the various architectures that are used in creating Pipelines.
What is Data Pipeline exactly?
A Pipeline refers to a set of steps that are implemented in an order to transfer data from one system to the next. The first step in a Pipeline is to extract data from the source. Each step generates an output which acts as the input to the next step.
This process continues until all steps are completed. In certain cases, steps could be executed simultaneously.
It usually has three major elements: a data supply, processing steps, final destination, and sink. Pipelines are a way for users to transfer data to a destination or make changes to it.
Pipelines may have the same source as their destination. However, it can only be used to transform the data in accordance with the requirements.
In recent years, however, the volume, velocity, and variety of data have all changed and become more complicated. Pipelines are now required to support Big Data for most businesses.
Businesses need to ensure their pipelines have zero data loss and high accuracy. High volumes of data could open up opportunities for operations such as Predictive Analytics or Real-time Data Reporting.
All three Big Data traits are supported by pipelines. Due to the high velocity at which data can be generated, pipelines need to be able to stream streaming data. This data needs to be processed by the pipeline immediately.
Why Use Data Pipeline?
Now that you understand what a data pipeline is, how can we use it to improve our lives? Data is increasing at a rapid pace and will continue growing. The most common use of pipelines is to inseam data. This data can then be transformed efficiently into data that is optimized for daily data generation.
This data can be used in Data Analytics, Machine Learning and other applications. Below are some of the uses of Data Pipeline.
- Transmitting the sales and marketing data to CRM platforms in order to enhance customer service.
- Streaming data to monitor performance and status from sensors
- You can accelerate the development process by bringing together all the data.
Types of Data Pipelines
Now you are familiar with ETL and Data Pipeline. Let’s look at different Pipelines. This is the complete list of data pipeline types.
- Bulk: Bulk processing of data can be leveraged by businesses that need to move large quantities of data at regular intervals. Batch processing jobs usually run on a schedule (for instance, every 24hrs or, in certain cases, when the volume exceeds a specified threshold).
- Real-time: Real-time pipelines are optimized to handle the data as soon it is received at the source. Real-time processing allows for the processing of streaming data such as data from the financial market or telemetry from the connected device.
- Open-source: These are pipelines that can be used to build a custom-made pipeline for their specific business or data needs. These pipelines are best developed and maintained by trained professionals.
But, these types can be used together. Pipelines can possess all characteristics from two different types. Pipelines could be Cloud-native Batch processing or Open Source Real-time processing.
Author & Publisher | Emily Forbes
An Entrepreneur, Mother & A passionate tech writer in the technology industry!