by • October 7, 2022
The pandemic saw the world rapidly pivot to mass remote working, forcing many companies to accelerate their digital transformation efforts. The result has been businesses facing an increasingly prevalent pain point in their operations — integrating and automating workflows, which calls for API led connectivity.
The 2010s saw the rise of an API-led approach as a central pillar of digital infrastructure, along with the industry’s embrace of SaaS. This has enabled highly sophisticated workflows that leverage a broad constellation of apps such as Salesforce, Slack, and Zoom. However, the sheer number of moving parts involved in these workflows has led to an explosion in complexity for organizations and their IT teams.
Many CIOs and IT leaders have adopted an API strategy to offset this complexity. By using APIs, teams can automate much of the life cycle of their workflows, improving user experience and organizational performance. This has seen the rise of dedicated infrastructure tools for orchestrating and configuring an API ecosystem.
But what does it mean to enable API-led connectivity? In this piece, we’ll dive into five significant trends powering this transformation in the API economy.
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A service mesh architecture is a rising trend in API-led connectivity. It allows developers to focus on adding business value rather than coding communication logic.
How is this possible? Service-to-service communication can overload resources as multiple services compete for access to resources. A service mesh combats this problem by abstracting the communication from each individual service to a layer within the app. Within this layer is the concept of proxies. These proxies route requests between services and optimize how each service works within the application ecosystem.
Proxies are often called “sidecars” because they run alongside the service. A service mesh architecture makes an application more resilient to downtime because proxies can reroute requests. The business value of this approach includes:
Many business leaders agree that repetitive, tedious tasks take employees away from more value-driven work. These monotonous tasks can eat away at productivity, whether it is processing invoices, onboarding, updating inventory, or data entry.
Robotic process automation (RPA) is intended to help IT teams circumvent these low-value and repetitive tasks. RPA consists of configurable “robots” or “bots” to capture data, analyze it, manipulate it, and trigger responses to other services. RPA’s goal is to provide an automated and systematic approach to repetitive tasks via a rules-based system.
Many people conflate RPA and artificial intelligence (AI). While these can look similar, there are critical differences between the two. RPA is software-based automation based on a predefined set of rules. AI, on the other hand, simulates human thinking.
For IT teams working on the digital experience for their organizations, the key takeaway of this distinction is that RPA requires far less computing and storage power than AI and tends to be far more consistent in its effect on the customer experience and internal processes.
Enabling enterprise scalability is key to a business remaining competitive and agile. Traditionally, a Center of Excellence (CoE) team that centralized resources and data handled this capability. New projects must follow procedures outlined by the CoE to get things done. Unfortunately, this approach created barriers to accessing information quickly and hindered progress.
Many companies realized a better solution was necessary, which eventually came in the form of Centers for API Enablement (C4Es). These cross-functional teams enable any business unit or project team to quickly access the information needed during the development process. Rather than just centralizing resources, C4Es are responsible for approaching and liaising directly with business divisions to build up and drive the adoption of APIs across an organization and ensure that APIs fit successfully into existing workflows.
This strategy decentralizes the management of data sources and services by empowering individual teams to leverage APIs rather than agglomerating responsibility for managing them into a single CoE team. This new way of thinking allows for shorter delivery cycles because there are no bottlenecks in workflows that leverage APIs, while enforcing better adherence to standards for API management across an organization.
APIs are valuable tools for sharing information between systems. They often start small but can quickly grow into a robust solution for enabling communication. However, what happens when the system grows so much that you get multiple services making repeated API calls to get a simple data update? The impact could be huge on your system.
An event-driven architecture helps scale the system to avoid overload. Event-driven architectures use events as triggers to spur communications between services, which reduces the number of API calls needed by an organization and thus saving on bandwidth, computing, and storage.
Because they maximize the performance of an API ecosystem while also cutting the resources needed to support them, event-driven architectures are often key pillars for organizational digital transformation initiatives. This is especially useful in modern applications that leverage a microservices architecture, which sees applications broken down into their smallest possible constituent parts, and then interact with each other via APIs. As a result, an event-driven architecture delivers robust connectivity between services through exchanging data and calls when required.
However, alongside this connectivity, event-driven architectures also allow for greater redundancy in an API management strategy. This is because the router strategy for event-driven architectures consists of a producer-router-consumer sequence: The producer pushes an event to the router, the router then pushes that event to the consumer, and the consumer eventually uses that data. As a result, the failure of a particular service is less likely to break the entire system.
An API-first approach to development projects speeds time to market and reduces cost through the reuse of APIs. With this approach, APIs are treated as “first-class citizens.” As such, the API gets developed first. The application is then developed off the API.
This approach is a radical change from the traditional model of code-first. A code-first approach is often time-consuming and results in a disjointed system based on a constantly changing set of requirements.
An API-first approach minimizes this problem by ensuring that functionality will work in the context of the APIs developed. This strategy also means development teams can work in parallel. Developers can simply develop mock APIs to test dependencies. Using this method for system development reduces cost through code reuse. It also speeds time to market.
The API-first revolution is being aided by a robust ecosystem of infrastructure solutions to help organizations manage their APIs. Some solutions like Dreamfactory go a step further and outright help teams generate new APIs from scratch automatically.
API-led connectivity is more than a technology. Instead, it should be seen as an IT paradigm and business strategy that keeps companies agile in a competitive market. Regardless of your line of business or your business model, API-led connectivity can be crucial to helping your organization improve and scale its workflows. This is especially the case for organizations that want to handle data-intensive use cases in IoT and artificial intelligence.
It is a strategy that will require companies to rethink their approach to projects. They must shift to a decentralized approach to managing data and resources. Companies must also embrace new software architectures that can support rapid growth. These five trends will continue to shape how organizations keep pace with constantly changing customer needs.
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