Cloud Data Integration
iPaaS Vendors Guide

iPaaS is a single platform that enables data consumers to implement application, API, data and process integration projects, involving any combination of cloud-based, and/or on-premise, endpoints. It develops, deploys, executes, manages, and monitors integration flows/processes across multiple endpoints.

Typical iPaaS integration scenarios include:

• Business-to-business (B2B) integration

• Application-to-application (A2A) integration

• Cloud service integration (CSI)

• Mobile application integration (MAI)

• Internet of Things (IoT) integration

Analyst firm Gartner considers an iPaaS to be enterprise-grade if it supports projects requiring high availability/disaster recovery (HA/DR), service-level agreements (SLAs), security, and technical support.

iPaaS standardizes how an enterprise’s current apps are integrated, and new apps are added, simplifying the movement of all data types across applications, while also delivering the required integration functionality.

iPaaS maintains, monitors, and updates processes dynamically across applications, which are in a constant state of flux (i.e., added, deleted, or changed). It lets data engineers and consumers build, manage, and maintain integrations simply.

iPaaS supports a wide range of data sources and delivery modes.

Core capabilities of iPaaS include:

• Data connectivity
  Data connectivity ingests data in any delivery mode, from any source, and then transforms it for delivery, in any format, to any target.

• Data/application integration
  Data integration builds and manages scalable data pipelines between source and target systems, for analytical and operational use cases.  

• Data orchestration
  Data orchestration unifies, transforms, and enriches data, from any source system into any target application, rapidly and simply.

• Data governance
  Data governance assures data quality, enforces data privacy, and makes the data easily accessible at massive scale.

• Data catalogue and data lineage
  A data catalog is embedded into the company's data fabric to classify and inventory data assets, and optically map information supply chains to reveal data lineage.

The platform should also be able to handle lifecycle management and monitoring (management of the cloud integrations), API management, and error handling. In short, it must be flexible and scalable enough to meet the highly demanding requirements of today’s data management.

Companies looking to work with the best iPaaS vendors face a complex mix of offerings, in which multiple solutions provide different advantages that can be complicated to compare and evaluate. As solutions consolidate and new technologies are born, the ecosystem grows more impressive, on the one hand, but harder to navigate, on the other.

Here are the main parameters enterprises should focus on:

• Business use cases: Does the iPaaS vendor answer the company’s intended use cases?
• Industry compatibility: Can the iPaaS vendor support specific industry needs?
• Workload support: Can the platform handle both analytical and operational workloads?
• Data management: How is the data organized, and how can this method affect the organization?
• Endpoint variety: How many endpoints are expected, and what is their nature? Can the vendor support these integration mechanisms and technologies (e.g., push, pull, streaming, messaging, ETL/ELT, etc.)?
• Scale and performance: Can the platform support large volumes of data and provide superior performance?
• Deployment capabilities: Can the vendor operate in hybrid, multi-cloud, and on-premise environments?
• Architectural support: Can the platform support various data management architectures, such as Data Mesh, Data Fabric and Data Hub?
• Cost: What are the pricing models, and do they fit the company's financial plans and budgets?
• API management: Does the iPaaS vendor offer API integration?
• AI capabilities: Does the platform offer autonomous, AI-powered features?
• Security: Does the iPaaS vendor meet all necessary compliance and security demands?
• Ease of use: How easy it is for business users to consume data, and for data engineers and data managers to modify data management flows?

It’s important to realize that while enterprises may be tempted to select a single platform that answers all of needs, there’s a good chance that multiple platforms would fulfill their requirements more effectively.

The following table lists the pros and cons of the industry’s leading iPaaS vendors:

Fabric as a Service (FaaS) is the perfect iPaaS solution for the enterprise because it transforms all the organization’s data, wherever it is, into business-centric data products.

Data products are business entities – such as customers, products, suppliers, orders, or anything else that’s important to your business – managed as a logical unit in terms of definition, and data instances.

Each data product is unified in its own secure Micro-Database™, continuously in sync with all source systems, and instantly accessible to all data consumers.

FaaS delivers a trusted, real-time view of any data product, deploys in weeks, scales linearly, and adapts to change on the fly. It supports modern data architectures – such as data mesh, data fabric, data hub, and multi-domain MDM – in on-premise, cloud, or hybrid environments.