This guide provides a side-by-side comparison of ETL (Extract, Transform and Load) and iPaaS (Integration Platform as a Service) capabilities to help craft your data integration strategy.
In cloud computing jargon, ETL is an abbreviation of three functions: Extract, Transform and Load. ETL is about extracting the data from the original data sources, transforming the extracted data in a standardized way and loading the standardized data into the data warehouse.
ETL is basically a data integration method that combines data from multiple data sources into a single, consistent data store and put into a data warehouse or other target system. The ETL tool results a centralized source of clean, consistent and ready to be used data for your IT team.
The ETL process is depicted below: (image credit to Microsoft)
Gartner defines Integration Platform as a Service (iPaaS) as a set of cloud services that lets you connect your systems, whether you live on-premises or in the cloud.
IPaaS carries modern set of data integration capabilities including the legacy capabilities of ETL (as well as ELT: Extract, Load, Transform). It plays the role of 'data hub' for the organization for real-time transactional integration. iPaaS tools are used to create real-time data pipelines and push information back into business applications such as CRM systems, ERP platforms, and other SaaS applications.
The iPaaS process is depicted below: (image credit to Tibco)
Though the two types of tools seem pretty similar, there are some notable differences.
iPaaS is the "successor" of ETL. The ETL process became a popular concept in the 1970s in data warehousing. Whereas the first iPaaS was launched in 2008 by Boomi.
ETL tools load data and transform data in batches, while iPaaS tools move data across systems in real-time.
ETL tools are often tailored towards on-premise systems, while an iPaaS can effectively handle on-premise and cloud as well as hybrid systems.
An ETL tool can only integrate your data, while iPaaS can integrate your data systems as well as your data.
iPaaS tools provide a platform that connects a cloud or on-premises applications, enabling businesses to bring data from different applications, systems and warehouses together to create business process workflows.
API management system enables businesses to build, manage, and extend APIs continually and in a secure environment, through unified management, versioning control, and finely-tuned security.
Both iPaaS and API management have different origin. iPaaS emerged from the cloud-oriented requirements whereas API management came from developer-oriented requirements to unlock and reuse the APIs for different endpoints.
Although both platforms have distinct capabilities and serve separate purposes, organizations are looking for unique cloud-based integration tools that come along with iPaaS solutions and the governing power of a full cycle API management system.
ETL is one of the data integration types commonly used in businesses for data governance. Data integration is essential to enhance data access, simplify data extraction, improve data quality and facilitate the data migration. ETL tools mainly pipeline the centralized data into an environment suitable for analytics.
Over the past few years, the role of ETL in data integration has significantly changed. Due to increasing demand of real-time data streaming, the organizations are relying more on real-time data analytics and monitoring. Hence traditional way of ETL does not meet needs of today's businesses. Nevertheless, ETL has been a vital component of data integration and an essential part of data warehousing and it is evolving with time.
Traditional ETL systems were in practice within organizations about a decade ago. In such systems, the frequency of data transfer between source and destination was as low as a few times in a day. The data used to reside in databases, files or data warehouses and the data integrations systems were based on relational databases which are static in nature. The traditional ETL systems were lacking scalability and required a heavy amount of IT expertise and developer-hours to write the scripts/apps to transfer the data.
With emerging technologies such as data lakes and flexible online storage schemas, there has been a paradigm shift from traditional data warehousing. The advent of cloud computing and cloud integration has radically transformed the role of ETL to fulfil today's data integration needs. Cloud-based data analytics warehouses for example, Amazon Redshift, Google BigQuery, and Snowflake with incredible data processing capability have changed the way businesses will interact with data warehousing indefinitely.
Integrate multiple systems with data connectors
Centralize Integrations on single platform
Connect relations via SQL, EDI, cXML, and JSON
Facilitate transitions from old systems to new one
Automate business processes and assist decision-making
Create data insights for Business Intelligence (BI) using Machine Learning (ML) & Artificial Intelligence (AI)
Monitor to enhance quality (quality hub)
Centralize information for data analytics
Enable self-service in reporting to reduce manual dependencies
Create a consistent enterprise data model
Streamline data migration
Automate manual workflows
Enable real-time monitoring and alerting
Train machine learning models to automate data management
Build data products for external consumption
ETL tools have been in use for over 20 years making them the most mature out of all of the data integration technologies. Today, ETL has various use cases and carry inherent pros and cons.
Suitable for large volumes of data movements involving complex transformation rules
Facilitate performance of BI apps by querying database instead of performing calculations and joining records for reporting
Make easy maintenance and traceability of data compared to hand-coded systems
Provide user-friendly view of data flows through graphical user interfaces (GUI) to minimize manual intervention
Self-service ETL tools enhance collaboration by enabling teams to develop and maintain organizational data warehouses
Automation and monitoring of data flows to ensure error free and resilient operations
Enhance data processing and reduce downstream data integrity issues
Enhance data governance by populating metadata
Assist in tracking data lineage. For example, perform an impact analysis by revisiting the data catalogs
Allow advanced data profiling by predicting how changes in the data schema can affect reporting
Require data oriented background in developers and data analysts
Not suitable for real-time or on-demand data access, where prompt response is needed
Installation and readiness before actual usage takes long time
Difficult to keep up with changing requirements
iPaaS is relatively new tool for data integration which carries certain benefits over ETL as well as some shortcomings.
iPaaS provides automation with no-code or low-code workflows for faster and hands-off integrations.
Real-time, high-volume connections and near real-time processing and is suitable for handling IoT workloads
iPaaS provides a platform for connecting different applications and systems, making it easier to integrate and automate business processes.
iPaaS eliminates the need for expensive on-premise integration solutions and can reduce the price associated with integrating systems.
iPaaS can easily scale to meet changing business needs, making it a flexible solution for growing companies.
iPaaS is typically cloud-based, which means it can be accessed from anywhere with an internet connection and eliminates the need for on-premise infrastructure.
iPaaS allows for fast deployment of integration projects, which can speed up the time it takes to integrate new systems and applications.
With iPaaS, the level of control over the integration process is typically limited, which can make it more difficult to customize or fine-tune integrations.
Because iPaaS is typically provided as a service, companies are dependent on the provider for support, maintenance and uptime.
As with any cloud-based solution, there may be security concerns around data privacy and protection.
Some iPaaS solutions may not support older or legacy systems, which can make it difficult to integrate those systems with newer applications.
Integration of certain systems may be complex and may require specialized knowledge which can increase the cost and duration of the project.
Both ETL and iPaaS can be used for data integration, and the best choice will depend on the specific needs and requirements of your organization such as the types of data sources you need to integrate, the complexity of the data transformations, and the scalability and flexibility of the solution.
ETL is a traditional data integration method which is a good choice for organizations that need to integrate large amounts of structured data from multiple sources and need to perform complex data transformations.
iPaaS, on the other hand, is a cloud-based application integration platform that allows for the integration of various systems and applications, including data integration.
iPaaS is a good choice for organizations that need to integrate data from multiple sources in a more flexible and scalable way, and that prefer to use a cloud-based solution. iPaaS can be more cost-effective and easier to use than ETL and can be integrated with other tools such as BPA, BAM, B2B and many more.