ETL Pipeline vs. Data Pipeline: What's the Difference?

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As the business landscape continually changes and integrates more technology, two concepts that will grow increasingly more important are ETL pipelines and data pipelines. These ETL pipelines and data pipelines play a significant role in moving data between systems. Understanding how these pipelines work and what the differences are between them is essential for companies as they increasingly rely on data for daily function.

Take a comment on social media, for example. It might be picked up by your tool for social listening and registered in a sentiment analysis app. At the same time, it might be included in a real-time report on social mentions or mapped geographically to be handled by the right support agent. This means that the same data, from the same source, is part of several data pipelines; and sometimes ETL pipelines.

Read on to learn more about data pipelines, ETL pipelines, their similarities and differences, and the significant role they can play in everyday business operations.

What is a Data Pipeline?

The term "data pipeline" can describe any set of processes that move data from one system to another, sometimes transforming the data, sometimes not. Essentially, it is a series of steps where data is moving. This process can include measures like data duplication, filtering, migration to the cloud, and data enrichment processes.

Example Use Cases for Data Pipelines

To perform predictive analytics
To enable real-time reporting and metric updates
To move, process, and store data

Types of Data Pipelines

There are four categories of pipelines. These categories are not mutually exclusive, meaning a data pipeline can have characteristics of more than one category.

Batch

Batch processing pipelines are used to move high volumes of data at regular intervals. These types of jobs run at regular intervals.

Real-Time

Real-time pipelines move data as soon as it is generated at the source.

Cloud-Native

Cloud-native pipelines are optimized for cloud-based data sources. Cloud-native pipelines are housed in the cloud with a third-party vendor rather than in-house. Using a cloud-native solution saves on infrastructure costs and frees developers to focus on more value-driven tasks.

Open-Source

Companies that need a low-cost alternative to commercial pipeline tools will find benefit in open-source pipeline tools. The downside to this approach is that you will need someone with the expertise to develop or extend the tool for your specific use cases.

Components of a Data Pipeline

Data pipelines contain several components, each with a specific purpose, that facilitate the movement of data:

Origin - The origin represents the source from which the original data resides.

Destination - The final destination is the ultimate point to which the data is transferred. The final destination can be a data store, an API endpoint, an analytics tool or more.

Dataflow - Dataflow refers to the movement of data between the origin and destination. One of the most widely used methods for moving data is Extract, Transform, Load (ETL).

Storage - A storage system refers to all systems used to preserve the data throughout the stages of data flow.

Processing - Processing includes all activities involved in moving the data.

Workflow - The workflow represents a series of processes along with their dependencies in moving data through the pipeline.

Monitoring - Monitoring ensures that all stages of the pipeline are working correctly.

What Is an ETL Pipeline?

ETL is an acronym for Extract, Transform and Load. An ETL pipeline is a series of processes extracting data from a source, then transforming it, and finally, loading it into a destination. The source can be, for example, business systems, APIs, marketing tools, or transaction databases, and the destination can be a database, data warehouse, or a cloud-hosted database from providers like Amazon RedShift, Google BigQuery, and Snowflake.

Example Use Cases for ETL Pipelines

To centralize your company's data, pull from all your data sources into a database or data warehouse
To move and transform data internally between different data stores
To enrich your CRM system with additional data

Related Reading: Building an ETL Pipeline in Python

Data Pipeline vs ETL Pipeline: 3 Key Differences

Data Pipelines and ETL Pipelines are related terms, often used interchangeably. But while both terms signify processes for moving data from one system to the other; they are not entirely the same thing. Below are three key differences:

1. Data Pipeline Is an Umbrella Term of Which ETL Pipelines Are a Subset

An ETL Pipeline ends with loading the data into a database or data warehouse. A Data Pipeline doesn't always end with the loading. In a Data Pipeline, the loading can instead activate new processes and flows by triggering webhooks in other systems.

2. ETL Pipelines Always Involve Transformation

As implied by the abbreviation, ETL is a series of processes extracting data from a source, transforming it, and then loading it into the output destination. Data Pipelines also involve moving data between different systems but do not necessarily include transforming it.

3. ETL Pipelines Run In Batches While Data Pipelines Run In Real-Time

Another difference is that ETL Pipelines usually run in batches, where data is moved in chunks on a regular schedule. It could be that the pipeline runs twice per day, or at a set time when general system traffic is low. Data Pipelines are often run as a real-time process with streaming computation, meaning that the data is updating continuously.

Why Use ETL Pipelines

ETL Pipelines are useful when there is a need to extract, transform, and load data. This is often necessary to enable deeper analytics and business intelligence. Whenever data needs to move from one place to another, and be altered in the process, and ETL Pipeline will do the job. ETL Pipelines are also helpful for data migration, for example, when new systems replace legacy applications.

In the extraction part of the ETL Pipeline, the data is sourced and extracted from different systems like CSVs, web services, social media platforms, CRMs, and other business systems. In the transformation part of the process, the data is then molded into a format that makes reporting easy. Sometimes data cleansing is also a part of this step. In the loading process, the transformed data is loaded into a centralized hub to make it easily accessible for all stakeholders.

The purpose of the ETL Pipeline is to find the right data, make it ready for reporting, and store it in a place that allows for easy access and analysis. An ETL tool will enable developers to put their focus on logic/rules, instead of having to develop the means for technical implementation. This frees up a lot of time and allows your development team to focus on work that takes the business forward, rather than developing the tools for analysis.

How Integrat.io Can Help

While ETL and data pipelines are both transforming how businesses interact with their data, they do not offer companies the same functions or services. When using an ETL pipeline, this signifies that a series of processes from data extraction, transformation, and loading will take place. When using a data pipeline, this signifies that the data will be transferred from one system to another but not necessarily transformed during the process. So, while the two terms may be used interactively, they are simply not the same thing.

While data pipelines and ETL pipelines are different, they both offer many benefits to companies by allowing for improved analysis and data management practices. If you’re looking for a way for your company to reap the benefits that ETL and data pipelines have to offer, look no further than Integrat.io. Integrat.io is a leading cloud-based ETL solution that provides your company with the tools to extract, transfer, and load data more efficiently.

Are you ready to discover more about the many benefits the Integrat.io platform can provide to your company? Contact our team today to schedule a 14-day demo or pilot and see how we can help you reach your goals.

ETL

ETL Pipeline vs. Data Pipeline:
What's the Difference?