iPaaS vs ETL: A Deeper Look Into the Data Integration Methods

Migrating data between various platforms is an important step in promoting data-driven decision-making. By harnessing the power of integrated data, you can generate effective insights that optimize business operations. It is essential to understand the available approaches that enable the implementation of data integration. Among these approaches, ETL and iPaaS are two of the most fundamental ones.

This article will comprehensively explain the key differences between iPaaS vs ETL, highlighting crucial aspects you must consider to fulfill your organization’s data workflow needs.

What Is ETL?

Extract, transform, and load, or ETL, is a process of retrieving data from data stores, making it analysis-ready, and loading it to a destination. The extraction segment of an ETL process involves accessing data from in-house databases or external sources, while the transformation step standardizes the data. Finally, the standardized data is migrated to a data system, like a data warehouse, for further analysis.

Pros of ETL

• Data Centralization: The ETL approach lets you centralize large volumes of data to facilitate better decision-making. By consolidating the data in a single repository, you can enhance data accessibility within your organization.‍
• Enhanced Data Quality: The transformation stage in ETL allows you to modify data so it is compatible with the target systems. This stage encourages you to ensure data quality by performing cleaning, normalizing, validating, and enriching steps.

Cons of ETL

• Maintenance Complexity: ETL data pipelines can be complex to manage and require extensive technical expertise to resolve errors.‍
• Handling Unstructured Datasets: You cannot directly load data to the target system without applying transformations. This makes it challenging to utilize ETL to work with complex, unstructured data.

What Is iPaaS?

iPaaS, or Integration Platform as a Service, is a cloud-based solution that empowers you to connect various software applications for seamless data flow. It facilitates data sharing between different tools, whether on-premise or cloud, with the help of API endpoints. This creates a secure way for applications to communicate, as API policies apply principles, such as authentication and authorization for the integration.

A recent study by Cognitive Market Research predicts that the iPaaS market will grow at a compounded annual growth rate (CAGR) of 28.9%. This growth is expected to occur from 2024 to 2031.

Pros of iPaaS

• Support for Lightweight Messaging: iPaaS supports modern messaging protocols and document systems, allowing different services to communicate without being tightly coupled.‍
• Improved Business Flexibility: As compared to traditional application integration methods, iPaaS reduces latency in data accessibility and improves business agility by allowing straightforward methods to integrate new applications.‍
• Cloud-Based Functionality: Most iPaaS solutions are deployed on a cloud, which makes it simpler to access from anywhere around the world, reducing infrastructure maintenance costs.

Cons of iPaaS

• Platform Dependence: As iPaaS is a service-based solution, companies are totally dependent on the service provider. This implies that if the provider encounters downtime or service issues, it can affect integrations, causing an abrupt stall in application connectivity.

iPaaS vs ETL: Key Differences

According to Google Trends, ETL dominated the search trends in 2024.

In the above graph, the blue line represents Google searches for ETL in 2024, while the red one indicates the results for iPaaS. But what are the key factors people consider when differentiating ETL from iPaaS?

Here’s a table summarizing the key differences between iPaaS vs ETL:

iPaaS Vs. ETL: In-Depth Comparison

Let’s explore some of the most crucial differentiating factors between iPaaS vs ETL:

Objective

iPaaS solution aims to allow integration between various applications, devices, and systems within your organization. It provides practical methods to integrate legacy systems with modern cloud applications and automate workflows to streamline business processes.

ETL, on the other hand, enables you to consolidate data from multiple sources into a single centralized repository for better accessibility. Performing ETL steps ensures that your organization's data is consistent, accurate, and complete.

Architecture

iPaaS is a suitable option for decentralized data sources, as it is designed for complex data architectures involving multiple endpoints. It has a distributed architecture with numerous components, including API gateways, message queues, and integration runtimes for seamless application communication.

In contrast, ETL is useful for synchronizing data between two points, source and destination, at one point in time. It involves using a dedicated staging area to transform the source data to make it compatible with the destination.

Big Data Management

iPaaS solution provides you the capabilities to manage data coming from diverse sources. It is a preferable option for applications that require employing complex business logic on the data. Rather than handling data directly, you can integrate iPaaS solutions with big data management tools, like Kafka streaming pipeline, to ensure efficient data processing.

ETL tools, in hindsight, provide a scalable way to manage big data coming from distributed systems in a structured manner. In the transformation phase, you can automatically modify raw datasets in highly informative data formats. To further enhance the ETL process, use tools that allow parallel processing to reduce the total time consumed when working with big data.

Use Cases for iPaaS

• IoT Integration: IoT, or Internet of Things, devices capture different measurements, such as water level, room temperature, or air quality. By integrating IoT devices with your existing data storage systems using iPaaS, you can configure an alert system according to predefined thresholds.‍
• Automate Business Process: Connecting various applications with the aid of iPaaS can enable you to automate and enhance business processes. For example, unifying your inventory database with an email marketing tool can trigger automated customer notifications based on shipment status or transaction.

Use Cases for ETL

• Migrating Data from Legacy Database: Utilizing ETL tools can promote historical data migration from a legacy database to a modern database. For example, you can extract historical patient and treatment data from a healthcare application, transform it, and load it into a modern data storage system.‍
• Customer Satisfaction: Migrating data from a customer relationship management (CRM) application into a data warehouse can empower you to improve customer experience. Segmenting and analyzing customer data is essential for creating marketing campaigns that cater to your customers' specific demands.

ETL vs iPaaS: What Should You Pick

Deciding between ETL vs. iPaaS depends on multiple factors, including the specific use case, cost consideration, and infrastructure availability.

For businesses that frequently interact with SaaS applications, iPaaS could be considered a better alternative. It provides a cloud-native approach with high scalability, API management, and integration capabilities. Another use case that makes iPaaS a preferable option is the automation of business processes. You can use an iPaaS solution to connect different applications, apply business logic, and minimize repetitive tasks.

On the contrary, the ETL approach is suitable for integrating data from diverse systems into a single data warehouse or database. This is an excellent approach if you are looking to consolidate data for analysis or reporting. The transformation step in ETL gives the flexibility to perform robust modifications on source data, empowering you to perform complex workflows. These processes ensure the powerful integration of disparate sources into a single source of truth.

However, your solution choice might ultimately boil down to the cost considerations. The overall price of incorporating iPaaS vs ETL solution into your data workflow varies depending on the specific tool you use. When calculating the total cost of a tool, various key aspects come into the picture, including:

• The volume of data to be replicated by the ETL tool or the application to be integrated by the iPaaS tool.
• Deployment model between on-premise, cloud, and hybrid.
• Infrastructure maintenance cost for on-premise solutions.

You must consider tools that offer all the functionality you need. As the efficiency of these tools relies on integration capabilities, it becomes essential for you to test them before purchasing a subscription.

Using Airbyte to Serve Your Modern Data Integration Needs

Modern data integration often requires a balance between iPaaS solutions for real-time application connectivity and ETL processes for consolidating data into centralized systems. Each approach has its strengths and limitations. By utilizing Airbyte, you can bridge the gap between these two approaches.

Airbyte is a data integration tool that enables you to transfer data between various data stores almost effortlessly. Providing more than 550 pre-built connectors, it lets you retrieve structured, semi-structured, and unstructured data from different sources, including Shopify, WooCommerce, and ActiveCampaign. If the connector you seek is unavailable, you can create custom connectors by leveraging Airbyte Connector Development Kits (CDKs) or Connector Builder.

Here are a few features that make Airbyte a good solution for iPaaS and ETL:

‍AI-Powered Connector Builder: The Connector Builder has an AI-powered assistant that reads through your preferred connector’s API documentation and auto-fills most configuration fields. This functionality simplifies your connector development journey.

‍Change Data Capture (CDC): CDC enables you to automatically identify source data changes and replicate them to the destination system. This feature lets you keep track of updates and maintain data consistency within your organization.

Along with these features, Airbyte offers a Python library, PyAirbyte, that allows you to utilize Airbyte connectors in a development environment. This library encourages you to store extracted data streams in prominent SQL caches, such as DuckDB and Snowflake. These caches are compatible with Python libraries like Pandas and AI frameworks like LangChain.

By converting SQL caches into Pandas DataFrame, you can perform complex transformations on the source data. This transformed data can then be used to generate visualizations using libraries like Seaborn or transferred to data warehouses for advanced analytics.

Conclusion

Choosing between iPaaS vs ETL requires you to thoroughly understand their pros and cons. iPaaS is advantageous for application integration, while ETL is favorable for consolidating data stored in dispersed systems. Considering use cases and overall costs can assist you in selecting the better option based on your workflow demands. Alternatively, you could also opt for tools like Airbyte, which provides you with a combination of iPaaS and ETL resources.