data pipeline

The term “data pipeline” refers to what?

To get information from one place (the source) to another (the destination), like a data warehouse, a system called a “data pipeline” is used. The data is transformed and optimised during the procedure, transitioning into a form suitable for analysis and the generation of actionable business insights.

Simply said, a data pipeline is a set of procedures followed to collect, organise, and transmit data. Modern data pipelines take care of the tedious, time-consuming work of processing and optimising continuous data imports automatically. Data is typically imported into a staging table for temporary storage, where it can be modified before being inserted into the reporting tables where it will be used.

Data pipeline advantages

It’s possible that your business processes vast amounts of information. You’ll need a bird’s-eye view of the entire dataset before you can conduct any kind of analysis on it. In order to conduct a thorough analysis, this information must first be retrieved from its various storage locations and then combined in a logical fashion.

In the event that any of the following describes your business, they are crucial.

  • Uses real-time analysis of incoming data
  • The information is kept in a remote server.
  • Data on dwellings from various sources


There are three main parts to every data pipeline: the data source(s), the processing steps, and the final destination.

The data came from the sources, which we will discuss first. Common examples of such sources include social media management tools, enterprise resource planning (ERP) systems like SAP and Oracle, customer relationship management (CRM) systems like Salesforce and Hubspot, and sensors embedded in IoT devices.

The steps involved in the procedure

Data is typically collected from its original locations, then refined to meet the needs of the enterprise, and finally stored in its permanent repository. Common processing operations include transforming, adding, augmenting, filtering, grouping, and aggregating data.

ETL vs. Data Pipeline

It is similar to data pipelines that extract, transform, and load (ETL) systems move data from a source, process it, and then load it into a destination. In most cases, though, ETL is only one step in a much longer procedure.

Structure of a data pipeline’s constituent parts

End-to-end data pipelines are necessary for effective data sourcing, data collection, data management, data analysis, and data use. This will allow you to create new opportunities in the market and provide cost-cutting business processes.

When considering a data pipeline, some important characteristics to look for are as follows:

Continuous and extensible data processing

Self-service data access and management that is made possible by the elasticity of cloud computing resources for processing disparate, unrelated datasets.

High availability and disaster recovery

There are many potential benefits to implementing modern data pipelines for your business, including faster decision-making, greater flexibility in the face of peaks in demand, and shorter times spent gaining insights and information.