What is an ETL Tool?​

• 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.​

What is iPaaS?​ ​

• 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.​

What's the difference between ETL and iPaaS?

• 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.​

What's the difference between iPaaS and API management?​​​

• 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. ​

Traditional ETL​​

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.​

Modern ETL​

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.​

What are some real-world use cases and data workflows?​

The seven most popular use cases of iPaaS tools are listed below: ​

• 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​

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There are eight top ETL use cases for building data pipeline in businesses:​

• 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​

Which is better for data integration: ETL or iPaaS?​

• 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.​