ETL vs iPaaS: Which one to choose? 

According to Productiv’s data, the average company uses 254 applications. That’s a lot of tools, and integrating them or their data is important to automate workflows, streamline reporting, or perform BI analysis. There are two ways to do that: ETL and iPaaS. 

ETL combines data from various sources into a single location. iPaaS can also do that, plus more. It connects different applications across cloud environments, enabling smoother data exchange between them. 

In this article, we’ll examine the key differences in ETL vs iPaaS and determine which best suits your use case.

What is an ETL tool?

ETL stands for Extract, Transform, Load — and it’s exactly what it sounds like:

• Extract: Collecting data from relevant sources 
• Transform: Cleaning the data and standardizing the schema/format to suit the warehouse
• Load: Moving the final, transformed data to the destination

An ETL tool automates these steps. It helps teams move raw data from all the scattered sources into a centralized, analytics-ready format.

For example, say you want to understand how users interact with your product. An ETL pipeline pulls data from your CRM, transforms it to match your warehouse structure, and loads it into Snowflake, where your analytics team uses it to analyze trends, churn patterns, or customer experience.

Learn more about building intelligent data pipelines : ETL platform.

What is an iPaaS?

iPaaS (Integration Platform as a Service) is a set of tools designed to connect software applications deployed across various cloud environments. Whether it’s syncing data between tools or moving it into a warehouse or data lake, iPaaS helps create smoother, more secure data flows across your applications.

Here is how it works: iPaaS platforms come with prebuilt connectors or API management features to integrate various applications. They often include ready-made connections for ERP systems, CRMs, and cloud storage services. 

Once connected, define transformation logic and rules that apply as the data moves. From there, the iPaas ensures the secure and end-to-end data delivery between applications, or to target warehouses and lakes for analytics purposes. 

What’s the difference between ETL and iPaaS?

The main difference in ETL vs. iPaaS is that ETL focuses on centralizing data into a single warehouse for easier access and analysis. iPaaS, on the other hand, can do this and also connect various business applications, enabling them to communicate and share data in real-time.

Let’s explore iPaaS vs ETL in more detail in the table below.

ETL	iPaaS
ETL tools typically process data in batches. But they offer near real-time capabilities by processing data in groups at low latency.	iPaaS moves data across systems in real-time.
ETL systems typically integrate data into a centralized location for analytical use.	iPaaS can integrate both data and software applications, enabling smoother data exchange between them.
ETL is best suits for handling large and complex raw datasets.	iPaaS is ideal for integrating various applications with complex business logic.
Traditional ETL tools were often built for on-prem systems. Modern tools now support both on-prem and cloud environments, so you can choose based on your infrastructure needs.	PaaS is primarily cloud-native but can effectively handle cloud, on-prem, and hybrid systems.
ETL tools come in license-based, usage-based, or subscription-based pricing models, depending on the vendor. For large-scale data processing, ETL is often more cost-efficient as you primarily pay for compute and storage.	iPaaS platforms are designed for event-driven orchestration. They often charge per task, action, or row. So, costs can quickly grow as you scale.

Factors to consider while choosing between an ETL and iPaaS 

Below are the key factors that differentiate iPaaS from ETL. Consider these carefully when choosing the right approach for your business.

Real-time capabilities

Typically, iPaaS offers real-time data integration, while ETL moves data in batches. Organizations that use different applications across various environments and need to connect them in real time often choose iPaaS. It ensures data stays up to date and enables real-time operations and decisions.

On the other hand, ETL processes typically run at scheduled intervals, moving data in batches. Unlike organizations that rely on real-time event data, companies that are fine with updates every hour or day tend to prefer ETL. That said, some modern cloud ETL tools offer near real-time capabilities by processing data in micro-batches, such as every 500 milliseconds. Though it internally follows a batch processing approach, the result can feel like real-time data movement.

Architecture 

iPaaS enables communication between decentralized data components. Since it supports modern messaging protocols and endpoint connections, applications can communicate without being tightly coupled. So it is well-suited for integrating complex systems that involve multiple endpoints across cloud and on-prem environments.

In contrast, ETL combines data from multiple sources into a target destination. It extracts data from a source, uses a staging area for transformations, and finally loads it to the destination. Similar ETL pipelines are created for each data source to move and consolidate information. This architecture works best for large, stable data formats that need to be combined and analyzed centrally.

Automation 

ETL automates data integration, while iPaaS automates workflows by orchestrating data movement and triggering actions based on events. Instead of waiting for scheduled batch updates, iPaaS listens for changes in one system and instantly pushes updates to connected applications.

For example, an iPaaS platform connects the different applications an e-commerce business uses. When a customer places an order, iPaaS updates the inventory system, triggers fulfillment in the warehouse app, and sends tracking information to the customer. One event keeps all systems aligned in real time.

In comparison, ETL runs automated data pipelines. Once you set up a process to extract, transform, and load data to a destination, it runs at scheduled intervals. The pipeline keeps the data in your warehouse fresh and ready for analytics.

Use cases 

iPaaS platforms often integrate SaaS applications such as CRMs, ERPs, accounting software, marketing platforms, and more. This is more common in workflow automation and API-based integrations. Moreover, iPaaS becomes especially valuable when real-time data is critical—for example, in IoT data streaming or cloud-native integrations.

ETL processes support centralized data warehousing and analytics by combining disparate data sources into a single location. For instance, they pull data from CRMs, inventory systems, and finance platforms into a warehouse like Snowflake. Teams then generate analytical reports or build predictive models such as demand forecasts, revenue optimization strategies, and recommendation engines.

Cost 

ETL tools typically charge based on the amount of data processed (per GB or TB) and compute time. In contrast, iPaaS platforms charge per task, event trigger, or API request.

This means if you perform a transform task on 1,000 rows, iPaaS may charge for each row. So, when you build workflows with many steps, frequent triggers, or large transformation tasks, your iPaaS costs can increase quickly.

On the other hand, ETL tools offer more predictable pricing. Since they charge per volume of data processed rather than per action, they scale more cost-effectively, especially for big data.

Find solutions to bottlenecks and pitfalls in your pipelines in this guide on overcoming common ETL challenges.

When to Use ETL Over iPaaS?

ETL is the go-to solution when you’re working with large volumes of raw data that need serious transformation to turn into actionable insights. 

Understand the difference between integration and transformation with this comparison of Data Integration vs ETL.

Migrating from legacy systems to modern data platforms. Enterprises running on legacy infrastructure struggle to integrate data into modern applications or warehouses because their legacy systems often lack APIs or standard integration capabilities. That’s where ETL comes in. It can extract data from those outdated systems, transform it into a modern format, and load it into warehouses like Snowflake for fast, reliable access.

Moreover, when your business relies heavily on historical data for decision-making. A solid ETL pipeline pulls together data from multiple sources, consolidates it, and gives your analytics team centralized access to it.

ETL has been around since the 1970s, and back then, building and maintaining ETL pipelines required high technical expertise. Every connection and line of code had to be written from scratch, with each phase — extract, transform, and load — manually implemented.

Today, ETL is much more accessible thanks to modern cloud-based tools like Hevo. These platforms offer graphical user interfaces that simplify the process of building and managing ETL pipelines. Users can define rules and transformation steps using drag-and-drop workflows or built-in SQL options for more control and flexibility.

Finally, whenever your primary goal is centralizing data for decision-making, analysis, reporting, or regulatory audits, ETL remains the best choice.

Dive into the structure and management of ETL logic in this breakdown of ETL code processes.

When to Use iPaaS Over ETL?

If you’re managing multiple SaaS applications across cloud environments, iPaaS is your best bet. It’s purpose-built to connect systems, apps, and devices, enabling seamless data flow across even the most complex cloud ecosystems.

While iPaaS can bridge on-prem and hybrid environments, it is primarily cloud-native, optimized for the way modern businesses run today.

Unlike ETL, which focuses on processing large volumes of data for analytics, iPaaS is ideal for real-time integrations. For example, when a customer places an order, iPaaS can log the transaction in your accounting software, update your inventory system and CRM, and send an order confirmation email. All of this happens automatically by connecting these applications and moving data based on defined logic.

If you need both real-time and batch processing, integrating on-premises and cloud systems, or connecting different IT applications along with data movement to warehouses, iPaaS offers the flexibility to support it all.

Learn how to improve your ETL processes with these proven ETL best practices.

Can You Use iPaaS and ETL Together?

Yes, and in many cases, you should. While they serve different purposes, iPaaS is built on the foundations of ETL, with a broader vision: instead of just integrating data, why not also integrate applications?

You can combine iPaaS and ETL to create a hybrid integration environment. In this setup, iPaaS handles real-time application integration, while ETL transforms and moves the final data into a warehouse. For example, iPaaS can detect an event such as a customer updating their profile, sync that update across CRMs and other tools, and then trigger an ETL job to ingest the updated information into the central data warehouse.

In some cases, iPaaS also handles data staging, pulling data from SaaS apps into temporary storage. From there, ETL processes take over to transform and load the data into its final destination. This approach is both cost-efficient and allows you to use each tool for what it does best: iPaaS for real-time orchestration and ETL for structured data processing.

Get a clear picture of how data moves through pipelines by exploring a typical ETL workflow.

Conclusion

Choosing between iPaaS and ETL requires understanding their strengths, weaknesses, and differences, which we have covered through this ETL vs. iPaaS blog. Put simply, choose iPaaS when you need to automate communication between your SaaS apps in real-time. If your goal is to automate data integration alone, ETL is the better fit.

With modern tools like Hevo Data, the ETL process has become much simpler and more cost-efficient while also supporting near real-time data movement.

Hevo offers 150+ pre-built connectors, allowing you to easily extract data from various sources and load it into your warehouse—without heavy engineering effort.

Still unsure if it’s right for you? Sign for a Hevo 14-day free trail and explore its full suite of features firsthand.

FAQs on ETL vs iPaaS