Data Integration vs Data Pipeline: What are the Differences?

As data becomes increasingly integral to business strategies, more and more companies across the globe are keenly pursuing means, tools, and other resources that will help them make the best use of the gold mine of data within their organizations. However, the continued evolution of technologies and the emergence of things like artificial intelligence, machine learning, and more the data landscape has been rendered slightly challenging to navigate. And that’s because of the complexity in the ecosystem, which has given rise to understandable confusion.

But the most common roadblocks people encounter on their journey of leveraging data has something to do with two things: Data Integration and Data Pipeline. Even though they are fundamentally distinctive things, people often tend to use the terms interchangeably or can’t tell the two apart. This nifty guide will help you out with that.

Data integration refers to the process of bringing together data from various sources into a unified platform. Data pipeline, on the other hand, is used to describe a collection of operations as well as tools that enable the extraction of data from a variety of sources before it is moved to a data warehouse or any other similar storage space. Now that we know the basics let’s further explore the differences between the two.

1. What are they?: Data integration is the process of extracting data, transforming it, and then loading it into a pre-decided output terminus. Whereas, data pipeline is a superset, which includes data integration, among other things. As mentioned above, the data pipeline encompasses tools and processes for the transfer of data from a source to a destination system. Though, it may not always involve data being transformed before being loaded into the destination system.
2. Purpose: Data pipeline deals with the transfer of data from multiple sources, such as databanks, processes across the business, business systems, and more into a target system, like a data warehouse. The aim here is to enable business analytics, business intelligence, and more eventually. Data integration, however, involves only a specific kind of data pipeline, wherein data is extracted, transformed, and loaded into a destination system.
3. How they work?: Usually, data integration operates in batches, i.e., data is moved between systems one lump at a time. Data pipeline, on the other hand, can be run either in installments or in real-time, depending on the business’ requirements. So, in case the company’s condition, at a given point of time, needs data to be continually updated, it would run the data pipeline in real-time to ensure that all events are managed as soon as they occur.

Data integration is what can be referred to as the strategy, and data pipeline is the implementation of that strategy, i.e., to execute data integration, you will need a data pipeline. It’s quite simple. But if you still find yourself needing expert help with data pipeline or perhaps you are looking for a solution for data integration using Talend, we recommend engaging the services of a trusted service provider.