Today, businesses all around the world are driven by data. This has led to companies exploiting every available online application, service, and social platform to extract data to better understand the changing market trends. Now, this data requires numerous complex transformations to get ready for Data Analytics. Moreover, companies require technologies that can transfer and manage huge quantities of data in real-time to the desired destinations. ETL and Data Pipeline are two such technologies that are in high demand among businesses to manage their ever-increasing data.

The term “ETL” describes a group of processes used to extract data from one system, transform it, and load it into a target system. A more general phrase is “data pipeline,” which refers to any operation that transfers data from one system to another while potentially transforming it as well.

This blog will introduce you to the basics of both ETL and Data Pipelines and provide their simple applications. It will further provide you with the key difference between these 2 tools that can help you understand the ETL vs. Data Pipeline discussion. Furthermore, it will explain the ideal use cases of both of these technologies. Read along to understand the differences between the Data pipeline and ETL and choose which is best for you!

What is a Data Pipeline?

Data Pipeline acts as the medium using which you can transfer data from the source system or application to the data repository of your choice. The architecture of a Data Pipeline is made up of software tools that collaborate to automate the data transfer process. A Data Pipeline can contain multiple sub-processes, including data extraction, transformation, aggregation, validation, etc. It is an umbrella term for all the data-related processes that can take place during data’s journey from source to destination.

The term “Data Pipeline” can refer to any combination of processes that ultimately transfer data from one location to another. This implies a Data Pipeline doesn’t need to transform the data during its transfer. This differentiates between a ETL vs Data Pipeline which is also a specific type of Data Pipeline. Generally, any of the subprocesses, like Data Replication, Filtering, Transformation, Migrations, etc, can be present in any sequence as part of a Data Pipeline.

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If yours is anything like the 1000+ data-driven companies that use Hevo, more than 70% of the business apps you use are SaaS applications. Integrating the data from these sources in a timely way is crucial to fuel analytics and the decisions that are taken from it. But given how fast API endpoints etc. can change, creating and managing these pipelines can be a soul-sucking exercise.

Hevo’s no-code data pipeline platform lets you connect over 150+ sources in a matter of minutes to deliver data in near real-time to your warehouse. What’s more, the in-built transformation capabilities and the intuitive UI means even non-engineers can set up pipelines and achieve analytics-ready data in minutes. 

All of this combined with transparent pricing and 24×7 support makes us the most loved data pipeline software in terms of user reviews.

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What is an ETL Pipeline?

Your organization generates and collects vast data quantities every day. Now, to gather any actionable insight from this enormous sea of data, you will need to: 

  • Extract and collect data spread across numerous sources that are in some way relevant to your business.
  • Perform Data Cleaning and Transformations on the extracted data to make it suitable for Data Analysis.
  • Load the transformed datasets into your chosen repository such as a Data Lake or a Data Warehouse and build a single source of truth.

Now, these processes work in a sequence to optimize your raw data into a form that is fit for analysis. However, if you are carrying out the above processes manually, various errors can occur. Your process code may throw sudden errors, certain data values may go missing, data inconsistencies can occur, and many other similar bottlenecks are possible when working with a manual ETL approach.

Businesses rely on an ETL pipeline to automate the three-step task and securely transform their data. An ETL Pipeline is made up of a group of tools that work to extract raw data from different sources, transform & aggregate the raw data and finally load it to your destination storage.  A good ETL Pipeline also offers you end-to-end management coupled with an error-handling mechanism.

ETL vs. Data Pipeline: Understanding the Difference

The term Data Pipeline and ETL Pipelines are almost used synonymously among Data Professionals. However, the differences between the Data Pipeline and ETL Pipeline are in terms of their usage, methodology, and importance. You can get a better understanding of the differences between the Data pipeline and the ETL pipeline by comparing the following differences:

Purpose

A set of procedures known as an ETL pipeline is used to extract data from a source, transform it, and load it into the target system. A data pipeline, on the other hand, is a little larger word that includes ETL as a subset. It consists of a set of tools for processing data transfers from one system to another. However, the data may or may not be transformed.

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Using manual scripts and custom code to move data into the warehouse is cumbersome. Changing API endpoints and limits, ad-hoc data preparation, and inconsistent schema makes maintaining such a system nightmare. Hevo’s reliable no-code data pipeline platform enables you to set up zero-maintenance data pipelines that just work.

  • Wide Range of Connectors – Instantly connect and read data from 150+ sources including SaaS apps and databases, and precisely control pipeline schedules down to the minute.
  • In-built Transformations – Format your data on the fly with Hevo’s preload transformations using either the drag-and-drop interface or our nifty python interface. Generate analysis-ready data in your warehouse using Hevo’s Postload Transformation
  • Near Real-Time Replication – Get access to near real-time replication for all database sources with log-based replication. For SaaS applications, near real-time replication is subject to API limits.   
  • Auto-Schema Management – Correcting improper schema after the data is loaded into your warehouse is challenging. Hevo automatically maps source schema with the destination warehouse so that you don’t face the pain of schema errors.
  • Transparent Pricing – Say goodbye to complex and hidden pricing models. Hevo’s Transparent Pricing brings complete visibility to your ELT spending. Choose a plan based on your business needs. Stay in control with spend alerts and configurable credit limits for unforeseen spikes in the data flow.
  • 24×7 Customer Support – With Hevo you get more than just a platform, you get a partner for your pipelines. Discover peace with round-the-clock “Live Chat” within the platform. What’s more, you get 24×7 support even during the 14-day free trial.
  • Security – Discover peace with end-to-end encryption and compliance with all major security certifications including HIPAA, GDPR, and SOC-2.
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ETL vs. Data Pipeline: Transformation Process

Data pipelines and ETL pipelines are similar in that they both involve the movement and transformation of data, but there are some key differences between them.

Data pipelines focus on the movement and transformation of data within an organization. They often involve the collection, processing, and storage of data in various systems, such as data lakes or data warehouses. Data pipelines can be used for a variety of purposes, such as data analytics, machine learning, and reporting.

ETL pipelines, on the other hand, are specifically focused on the extraction, transformation, and loading of data from one system to another. ETL pipelines are often used to move data from transactional systems, such as databases, into a data warehouse or data lake for reporting and analysis.

ETL vs. Data Pipeline: How They Run?

The working methodology of data pipelines and ETL pipelines can differ depending on the specific use case and the systems involved. However, in general, data pipelines and ETL pipelines have some key differences in their working methodology.

Data pipelines often involve a continuous flow of data, where new data is collected, processed, and stored in near real-time. This allows for more dynamic and up-to-date analysis and reporting. Data pipelines also often include data quality checks, validation, and error handling to ensure that the data is clean and accurate.

ETL pipelines, on the other hand, are typically run on a schedule, such as daily or weekly. They extract data from transactional systems, transform it to fit the structure and format of the destination system, and then load it into the destination system. ETL pipelines often include more complex transformation processes, such as data mapping, data cleaning, and data deduplication.

In summary, data pipeline are more dynamic and incremental with real-time data movement and processing while ETL pipelines are more batch-oriented, with a schedule of data extraction, transformation and loading.

ETL vs. Data Pipeline: Examples

The following examples are of use cases in ETL vs. Data Pipeline that a Data Pipeline can support but are not achievable via any ETL Pipelines:

  • Providing you with real-time reporting services.
  • Providing you with the facility to analyze data in real time.
  • Allowing you to trigger various other systems to operate different business-related processes.

ETL Pipeline finds applications in various fields depending on their subtasks of Extracting, Transforming, and Loading data. For example, if a company requires data present in different Web Services, Customer Relationship Managers (CRMs), Social Media Platforms, etc., it will deploy an ETL Pipeline and focus on the extraction part.

Similarly, the Data Transformation aspect of an ETL Pipeline is essential for applications that need to modify their data into a reporting-friendly format. Furthermore, Data Loading finds applications in tasks that require you to load vast datasets into a single repository that is accessible to every stakeholder. This implies, that an ETL Pipeline can have a multitude of applications based on its various sub-processes.

Choosing The Right Pipelines for Your Customer Data

Choosing between a data pipeline or an ETL pipeline depends on your specific use case and the systems involved. Both data pipelines and ETL pipelines can be used to move and transform data, but they have different strengths and are optimized for different types of use cases.

In summary, if you are looking to move and transform data within your organization for purposes such as data analytics, machine learning, and reporting, a data pipeline may be a better fit. However, if you are specifically looking to move data from transactional systems, such as databases, into a data warehouse or data lake for reporting and analysis, an ETL pipeline may be a better fit. It’s important to evaluate your specific use case and the resources available to you before making a decision.

Getting data from many sources into destinations can be a time-consuming and resource-intensive task. Instead of spending months developing and maintaining such data integrations, you can enjoy a smooth ride with Hevo Data’s 150+ plug-and-play integrations (including 50+ free sources).

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Saving countless hours of manual data cleaning & standardizing, Hevo Data’s pre-load data transformations get it done in minutes via a simple drag and drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form. 

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Abhinav Chola
Research Analyst, Hevo Data

Abhinav is a data science enthusiast who loves data analysis and writing technical content. He has authored numerous articles covering a wide array of subjects in data integration and infrastructure.

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