Data Pipeline vs. ETL: Optimize Data Flow (Beginner's Guide)

March 14, 2024
15 min read

Are you having trouble finding efficient processing methods to leverage your data with growing demands? Regarding this topic, two terms often come to mind: Extract, Transform, Load (ETL), and data pipeline. Both are the foundation for data gathering, movement, processing, and analysis. 

The terms are often used interchangeably, but that is not right, as ETL and data pipeline are very different. For instance, data pipelines don’t necessarily have complex data transformations but ETL does. 

In this article, you will learn the distinction between data pipeline vs ETL in detail. 

What is a Data Pipeline?

Data pipeline is a process for efficiently moving and managing data from one operational source to another. It is an umbrella term for the category of migrating data between systems. Before data is moved from one system to another, it usually undergoes some data processing. This includes processes like data transformation, validation, and standardization. However, data processing is not always mandatory. In other words, you can have data pipelines without any transformation stage.

Data pipelines act as the "piping" for data management projects, which contain three key elements: a source, processing steps or logic, and a destination. Sources are where you get the raw data; the processing logic is the whole logic of managing data from the source, and the destination is where you centralize source data. 

Generally, there are two types of data pipelines: batch processing and streaming. Batch processing involves the execution of data jobs at scheduled intervals. In contrast, streaming data pipelines process data in real time as it gets generated. 

What is ETL?

ETL, or Extract, Transform, and Load, is a type of data pipeline. It is a set of processes to extract data from one system, transform it into a standard format, and load it into a target repository. In simple terms, it is a data integration process with fixed steps.

  • Extract: The initial process in ETL is pulling data from a source such as a database, a flat file, or a cloud platform holding data for systems such as CRM tools. The goal of this step is to read and process data sources and store them in a staging area or temporary storage. 
  • Transform: The process of converting the structure of the raw data set to match the storage system or centralized repository. The process may involve aggregation, cleaning, validation or creation of new data attributes. 
  • Load: Lastly, the data set is placed into a storage system, a data warehouse, a database, or an application. It can involve creating appropriate tables and schemas, overwriting existing data or writing to a file. 

This process is ideal for small data sets which require complex transformations. However, the more modern ELT process is more appropriate for a more extensive unstructured data set. 

Data Pipeline Vs ETL: Key Differences

Attributes Data Pipeline ETL
Process Generalized process for moving and managing data from data sources to target systems. Specific process for extracting data from sources, transforming it according to business needs, and loading it into a target system.
Data Transformation Ideal for basic or no transformations. Can perform complex transformations with advanced business logic.
Latency Low latency. Higher latency.
Post Loading Data pipelines can extend beyond mere data loading. In these pipelines, loading can initiate further actions like activating additional processes, or triggering webhooks. ETL concludes its role once the data is extracted, transformed, and loaded into a central repository.

Data Pipeline Vs ETL Pipeline: In-Depth Comparison

Here is a difference between data pipeline and ETL: 

Data Pipeline vs ETL: Use Cases

Here are some of the primary use cases of ETL: 

  • Enabling data migration from operational systems to a centralized repository. 
  • Providing a stable dataset for analytical activities to quickly access a single, pre-defined analytics activity given that the data set has already been structured and transformed. 
  • Complying with HIPAA, GDPR, and CCPA standards so that you can correct or omit any sensitive data before loading it into the target system. 

Some of the key use cases of data pipelines are mentioned below: 

  • Connecting two or more operational or storage systems to move data from and to the systems. 
  • Processing real-time data streams from sources such as IoT devices, social media, and flat files and making it available for analytics the right way. 
  • Building event-driven systems to trigger data pipelines to react to specific events in real time. This involves processing user interaction, business transactions, or workflow-based rules or conditions. 

Data Pipeline vs ETL: Data Processing 

In ETL, data is collected and stored at a staging area, and then it is processed to be stored in a centralized system. ETL process can be carried out for both real-time and batch processing. For real-time processing, you need to implement complex transformation steps to ensure the data is suitable for real-time analysis. Real-time data processing or stream processing includes processing data as soon as it arrives. It allows you to take immediate action on data, which is critical for scenarios requiring timely data synchronization. The challenge with real-time processing is it has a more complex infrastructure and can be more resource-intensive than batch processing. 

The batch processing approach is ideal for situations where you don't require real-time data, and operational systems can afford downtime for data extraction. However, while processing data in huge sets, there can be significant delays between data extraction and analysis availability. Overall, ETL data processing is highly used for having quality data for analytics.

On the other hand, data pipelines are not limited to analysis. While you can use data pipelines for analytics, other uses of data pipelines can include creating backups that don’t require transformation. However, similar to ETL, data pipelines can process data in real-time as well as in batches.

Data Pipeline vs ETL: Data Transformation

In ETL, data transformation is the mandatory second step just after extraction. The step includes cleaning, validating, and restructuring data to ensure it is in the right format for analysis. Transformation is usually complex and time-consuming when dealing with large datasets from diverse volumes. However, the process ensures that data is accurate, consistent, and in standard format supported by the centralized repository. 

In a data pipeline, data transformation is optional. If required, you can make it happen at any stage. It can happen at the source, during transit, or at the destination. This flexibility allows you to perform transformation conveniently, resulting in more efficient processing. However, the challenge with this approach is it may require different technologies and tools at different stages of transformation.

Data Pipeline vs ETL: Data Storage

ETL stores data in a centralized repository such as a data warehouse or analytical platform. The centralized system serves as a single source of truth for reporting, analytical, and business intelligence purposes. Maintaining a data warehouse can be complex and costly, needing significant storage capacity and processing power. 

Contrarily, the data pipeline does not mandatorily require a centralized storage system. You can store data in different data lakes, databases, or any staging area in the cloud. This gives you the flexibility to have a more distributed and scalable architecture. However, with this flexibility, ensuring data security, integrity, and consistency across multiple storage systems can be challenging. 

Build Automated Pipelines Using Airbyte

Now that you know about data pipelines vs ETL pipelines in detail, you might want to take action on the knowledge and build one yourself. However, creating a customized data pipeline for the data source and destination of your choice can get challenging. That is where tools like Airbyte come into use. 


Airbyte is a data integration platform that takes an ELT approach to move data from disparate sources to destinations such as data warehouses. The platform offers the largest catalog of over 350 pre-built connectors. Using these connectors, you can automate the creation of a data pipeline for any data source or destination of your choice. 

However, that's not all. Airbyte has many cutting-edge features, such as an intuitive user interface, orchestration capabilities, and robust data governance to handle all your data pipeline needs.

Key features of Airbyte include: 

  • Custom Connectors: In the extensive library of connectors, if you still can't find your unique data source, Airbyte allows you to build custom ones. Using its connector development kit, you can build connectors of your choice to suit specific business requirements with just a few clicks. 
  • Scheduling and Monitoring: Airbyte offers cutting-edge scheduling and monitoring features for data replication and lets you choose between batch or real-time updates. This feature and intuitive user interface allow you to schedule data pipelines and track data performance easily.
  • Flexible Pipelines: Airbyte provides different ways to build pipelines, including UI, PyAirbyte, Terraform provider, and APIs. This allows you to automate the pipeline creation and customize using custom code. 


While data pipeline and ETL serve similar purposes, they differ significantly in their data integration and management approach. By understanding data pipeline vs ETL and the key differences mentioned above, including differences in use cases, data processing, transformation, and storage, you can choose the right strategy for your organization. 

We suggest using Airbyte to automate data pipeline creation. More than 40,000 engineers use Airbyte to replicate data from data source to destination. Sign Up with Airbyte today!

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