The pandemic shall leave behind a trend of increased adoption of cloud, automation, and analytics. After all, global businesses want to perform better in similar circumstances ahead. Like all modules of the ‘cloud’, IPaaS too is scaling and addressing business agility needs.
As a result, the global market is propelling and breaking all records. As per Market Watch, the global integration-platform-as-a-service (iPaaS) market is forecast to grow and touch USD 48.3 billion (€43.68 billion) by 2030. For the forecast period (2022-2030), the growth shall record a CAGR of 37.4% which is the highest ever for this line of business, says Yash Mehta, an IoT and big data science specialist.
However, the road to quicker adoption may not be that easy. This is because the market was not ready for an immediate transition to cloud (COVID). That has eventually left a widening gap in the availability of skills. Here’s a quick analysis of other challenges:
Inability to achieve easier & quicker integration
It doesn’t come as a surprise to see enterprises building apps that are chosen and then deployed by these businesses in silos. But the challenge is to integrate these with other apps in a way that makes data sharing a piece of cake across the organisation. There are also several other business-critical processes that these ‘best of breed’ apps must support.
These include different applications that work across various departments and include features like order fulfillment, quote-to-cash, procure-to-pay, item management, etc. You can also expect a large volume of data of different types and formats which will flow through these different apps.
Earlier, businesses used a combination of custom programming, EAI (enterprise application integration) implementations like SOA (service-oriented architecture), and middleware to integrate their business processes for operations. Although these solutions make things work fine at the same time, they can be time-consuming and less cost-effective. Another problem is that they leave the business dependent on certain data silos and so data cannot be shared among different users.
Ensuring architectural agility
When using iPaaS flows, every new integration that is added has to be rewired in the flow in order to include it. This means that performance characteristics of the new integration point like latency and downtime potential are put upon the already running flow. These types of integrations generally have a high impact and take longer to market because introducing new services and features can make their system less robust and responsive.
Maintaining a high level of availability and performance
All producer and consumer apps should be connected directly to every integration as this makes sure they are aware of one another and also their specific address and location. Because of this interdependence, the availability of both the consumers and producers can be severely impacted easily by an outage, bug, or change in a negative way.
To put it simply, a robust and future-ready iPaaS system should achieve the following:-
- Quicker and accurate integrations
- Achieve better results with minimum effort Complete more enriched integrations in lesser time and without any new integration functionalities.
- Abbreviate Integration Costs Lessen dependency upon coders for custom integrations. Offer monthly, annually or customised subscription plans in the best interest of the customer.
- Fully optimise B2B integrations to sync with different information exchange processes for different applications.
- Embed API management to eliminate publishing customer APIs.
Enters E-iPaaS: Faster time to value
As explained by Gartner, EiPaaS aims to simplify the process of creating complex integrations and shifting integration management from IT to data consumers. It achieves so by simplifying the guidelines for the users. Additionally, it ensures end-to-end security protocol implementation such as user authorisation and authentication, fraud detection, and compliance with privacy regulations such as GDPR.
With a focus on resolving the challenges mentioned previously, E-iPaaS operates on the following principles:
- Data teams should ensure uninterrupted design; integration and delivery of applications regardless of the apps are built internally or on the cloud.
- Enable continuous testing, deployment, and automation of applications with a focus on faster time to market.
- Use the most appropriate iPaaS tool that integrates with cloud platforms such as AWS, Azure, others as well as locally hosted VMs.
Data fabric-as-a-service (FaaS) – A modern approach to E-iPaaS
In the pursuit of supporting high-volume and high-scale operations in real-time, organistions are embracing the FaaS approach. So far, it has helped them achieve split-second and end-to-end response times in bi-directional data movement between source and target.
Let’s understand that enterprise iPaaS calls for an entirely different approach. E-iPaaS offers set rules that focus on shifting integration management to customers directly instead of IT. Vendors can now be present on the public cloud along with being actually present at the site. This hybrid approach is followed by modern E-iPaaS vendors as it is based on a FaaS (Fabric-as-a-service) model. FaaS pays attention to the ease of distribution and uses as it works on a principle that identifies data assets into data products.
In the data fabric model, all entities are unified in their personal micro-database which is always in sync with the source system that is available within an ecosystem. This makes data accessible to all the users within the environment.
K2View is probably the data fabric that has been deployed on a public cloud and offers both on-premise and other hybrid deployments options.
As a highly effective E-iPaaS solution, their fabric enables an enterprise to build a data schema once and use it for multiple use cases including pipelining, TDM, customer 360, and others. Moreover, the data management for operational and analytical workloads has delivered data sets into consuming applications. Other use cases include pipelining into data lakes and warehouses for analytics.
As expected, this has led to more data fabric solutions in this direction. The idea is to integrate, transform, enrich and prepare data into an aggregated single platform.
Conclusion
So far we discussed the major challenges in adopting iPaaS including the complexity of system architectures, uncertain costs, unavailability of talent and others. Next, we introduced E-IPaaS and its role in simplifying the challenges. Ultimately, we moved to spotlight on Fabric-as-a-service solution that could rewrite the course of the cloud industry.
Did you use E-IPaaS? How is it helping you?
The author is Yash Mehta, an IoT and big data science specialist.
Comment on this article below or via Twitter: @VanillaPlus OR @jcvplus
