Data Pipeline: What Is It? A Comprehensive Explanation

Numerous avenues exist for big data to influence our reality. All of our work is powered by data. The systems must make sure that data is flowing between various systems in a sufficient, accurate, and, most importantly, consistent manner. The term “pipeline” refers to a group of tasks and technologies used to transfer data from one system to another while maintaining the same level of data processing and storage. Information is simple to manage and store in a different way once it has been transferred to the destination system.

Origin

A location in a specific pipeline where data can be entered. IoT devices, transaction processing programs, application programming interfaces (APIs), and social media are all examples of origins. Other examples of origins include storage systems such as data lakes and data warehouses.

Destination

A destination is an endpoint that the data must finally be sent to in order to complete the transfer. The application scenario of the data pipeline is what determines the destination. It also has the capability of running analytical tools and providing power for data visualization.

Processing

A series of actions and stages involves collecting data from several sources, conserving it, altering it, and then delivering it to a certain location. As it relates to the data flow, data processing is concentrated on executing this pattern. Data can be consumed by being extracted from a source system, copied using data replication, or even simplified.

Work Flow

A workflow in a pipeline describes any series of tasks and how they relate to one another. Any unit of assigned work that will carry out a certain defined task related to data is referred to as a job. Upstream refers to the point at which data enters the pipeline, and downstream designates the location to which it will ultimately go. Like water, data moves down the pipeline. Keep in mind that prior to starting the downstream duties, the upstream chores must be finished.

Monitoring

This verifies that a data pipeline and all of its steps are functioning properly. This involves ensuring that efficiency is maintained even as the data load increases, as well as ensuring that data continues to be consistent and correct even as it is passed through a variety of processes without any information being lost.

Use Cases of Data Pipeline

Data management is becoming an ever-increasing priority due to the growth of big data. Although a data pipeline can perform a variety of tasks, the following are some of its most common uses in the market:

• Data visualizations use graphics like charts, infographics, plots, and motion graphics to depict any type of data. Complex information can be communicated visually much more effectively with visualizations.

• Utilizing data visualization to enumerate the characteristics, exploratory data analysis is used to evaluate and investigate data sets. It teaches data scientists the most effective technique to work with data sources so they may finally find anomalies, test hypotheses, identify trends, and even verify presumptions.

• Machine learning is a branch of artificial intelligence that focuses on using data and algorithms to imitate how the human brain thinks and makes decisions. In data mining projects, algorithms help unearth various important insights by making predictions using statistical techniques.

Frequently asked questions:

Why do we need a data pipeline?

Data pipelines allow data to move between different systems, such as from an application to a data warehouse, from a data lake to an analytics database, or into a system for processing payments. Data pipelines can alternatively have the same source and sink, in which case their only purpose is to modify the underlying data collection.

What is the first step of a data pipeline?

The data pipeline architecture’s initial phase is data ingestion. Data ingestion is the process of transferring data from the source system where it is created to the target system where it can be accessed by all users, including BI analysts, developers, etc.

What is the difference between pipeline and data flow?

Data travels across a network of pipes from one component to the next. Each pipe has a left-to-right data flow. A “pipeline” is a group of pipes used to join parts of a protocol together.