ETL Pipeline and Data Pipeline are two concepts growing increasingly important as businesses keep adding applications to their tech stacks. More and more data is moving between systems, and this is where Data and ETL Pipelines play a crucial role. 

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.

In this article, we will inspect the difference between Data Pipelines and ETL Pipelines.

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Table of Contents

  1. What is a Data Pipeline?
  2. What is an ETL Pipeline?
  3. Data Pipeline vs ETL Pipeline: 3 Key differences
  4. Why Use ETL Pipelines?

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.

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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 RedShiftGoogle 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

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.

4. Conclusion

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While ETL and Data Pipelines are terms often used interchangeably, they are not the same thing. ETL Pipelines signifies a series of processes for data extraction, transformation, and loading. Data Pipelines can refer to any process where data is being moved and not necessarily transformed. 

The purpose of moving data from one place to another is often to allow for more systematic and correct analysis. Well-structured data pipelines and ETL pipelines improve data management and give data managers better and quicker access to data. 

Xplenty is a cloud-based ETL solution providing simple visualized data pipelines for automated data flows across a wide range of sources and destinations. Our powerful transformation tools allow you to transform, normalize, and clean your data while also adhering to compliance best practices. 

Learn more about how our low-code ETL platform helps you get started with data analysis in minutes by scheduling a demo and experiencing Xplenty for yourself.