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iPaaS vs ETL

Written by
Carla Hetherington
Published on
June 19, 2023

The 21st century is often dubbed the information age, where data is ubiquitous and has become the guiding force behind business decisions and goals. As such, the demand for data integration has never been higher. New data integration strategies are developed daily, and they all coexist. This blog will tackle two prominent data integration solutions, ETL (Extract, Transform, and Load) tools and the iPaaS (integration Platform as a service). While they are both favorites amongst users, it is essential to understand how they differ from one another and what purpose they serve. Let's dive into the nitty-gritty!

What is an iPaaS?

An iPaaS is a cloud-based integration platform that enables companies to connect any two or more systems, SaaS solutions, cloud applications, or data sources from one central hub. As such, it provides a range of tools and services for connecting, mapping, and transforming data between various applications and systems. It enables data synchronization and workflow automation. The best iPaaS platforms can connect the best of both worlds- on-premises apps/legacy systems and cloud apps within a single firm or between diverse businesses.

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What is ETL?

ETL stands for Extract, Transform, Load and is a process frequently used in data integration and data management tasks. It refers to the process of extracting data from various sources, transforming it into a desired format, and loading it into a target destination such as a database or data warehouse.

How does ETL work?

Here's a breakdown of each step:

  1. Extract: Data is extracted from multiple sources, which can include databases, spreadsheets, APIs, log files, or any other structured or unstructured data sources.
  2. Transform: Once the data is extracted, it undergoes transformation operations such as cleaning and filtering the data, removing duplicates, aggregating or splitting data, and applying any necessary data manipulation or calculations. The transformed data is prepared to meet the requirements of the target destination.
  3. Load: After transforming the data, it is loaded into the target destination, such as a database or data warehouse. The data is organized and stored in a structured manner, ready for analysis or further processing.

What is the purpose of ETL?

ETL plays a crucial role in integrating data from various sources, harmonizing it, and making it readily available for analysis, reporting, and decision-making purposes.

In the olden days, traditional business procedures were built upon the concept of independent data processes resembling static stations on an assembly line. Data generated in one place would gradually flow to the next station along the line. Initially, IT systems were designed to mimic this value chain process. However, the introduction of ETL revolutionized this scenario. ETL enabled data integration across different systems by replicating operational data into data warehouses for archiving and analytics. This breakthrough facilitated seamless data flow and improved the integration between systems.

What are the challenges of ETL?

With rapid technological advancements, ETL processing, and businesses/organizations faced two significant challenges that could no longer be ignored.

The first challenge was the exponential growth of data and the associated costs of storing it. For instance, if you have 10 data records in the source system and use ETL processes to replicate and distribute it to ten downstream systems, you end up with 10 times the data volume for storage and maintenance. This duplication could be avoided if the downstream systems could directly access the data from its source.

The second problem that arose was the difficulty of keeping these replicated data up to date. Many ETL operations rely on batch processing, where a batch of transactions accumulates and is periodically pushed downstream. In simpler terms, it's like a stack of letters in an outbox that is picked up and distributed by the postal service once a day or week. While batch processing worked well for manual business processes, it became outdated with the real-time demands of modern business operations.

What are the differences between iPaas and ETL?

The first notable distinction amongst the two is their scope and functionality. For starters, an iPaaS is a comprehensive integration platform that goes beyond the traditional ETL process as it offers a broader range of integration capabilities, including real-time data integration, application integration, API management, workflow automation, and more. ETL, on the other hand, specifically focuses on the extraction, transformation, and loading of data from source systems to a target destination.

The second difference is their deployment model. While an iPaaS is typically cloud-based and provides a platform that is hosted and managed by a service provider, ETL tools can be deployed in various ways, including on-premises or in the cloud, and can offer both standalone solutions or be part of a larger data integration suite.

Another key difference is their flexibility and scalability. iPaaS platforms are designed to be highly flexible and scalable, allowing organizations to adapt and integrate with diverse systems and applications, both within their organization and with external partners. ETL tools, on the other hand, often have a narrower focus on specific data integration tasks and may require additional customization or development to handle complex integration scenarios.

A fourth difference is the user-friendliness. While an iPaaS provides user-friendly interfaces, pre-built connectors, and visual tools for easy integration that are accessible to non-technical users, ETL tools can have a steeper learning curve and require more technical expertise.

Another difference worth mentioning is the difference in integration times. While iPaaS excel in real-time Integration and enable data exchange across systems and applications instantly, ETL is mainly focused on batch processing, with scheduled intervals for data extraction, transformation, and loading.

Lastly, the extensibility of their ecosystem is noteworthy. An iPaaS traditionally offers marketplaces or ecosystems with a wide range of connectors, APIs, and integration tools for extending functionality. On the other hand, ETL may have a more limited ecosystem, and relies on custom development for extending integration capabilities.

Why do businesses often choose an iPaaS over ETL?

Simply put, an iPaaS can do everything that an ETL tool can, and much more.

An iPaaS provides a comprehensive integration platform with broader capabilities for integrating diverse systems and data sources, including real-time integration that ensure up-to-date and synchronized data. Additionally, an iPaaS simplifies integration tasks with user-friendly interfaces, pre-built connectors, and APIs for extended functionality as well as bigger marketplaces and ecosystems. ETL, on the other hand, specifically concentrates on data extraction, transformation, and loading, often requiring customization for complex integration needs.

In view of this, it is no wonder businesses are migrating from ETL tools to an iPaaS. Don’t be the last one to hop on the wagon! Contact one of our specialists today and plan your integration strategy for tomorrow!

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