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"REST" In Peace: Why GraphQL Is Better Then REST API

Brian DeWyer
Reveille Software

Many organizations rely on enterprise content systems to deliver their information efficiently. However, these content services (heavy applications) must integrate with various applications and services to ensure smooth operation. This integration is not an afterthought; it's a critical component for enabling consistency, collaboration, and streamlining the entire data interaction process. And with no-code/low-code themes in Robotic Process Automation (RPA) and Intelligent Automation product sets typically seen in modern content services applications, less is more to accelerate solution delivery.

To accomplish application integrations, IT developers will increasingly have a choice between GraphQL and REST APIs for web client interfaces. Facebook developed GraphQL, now an open-source query language and runtime for APIs. By contrast, Representational State Transfer Application Programming Interface, or REST API, is an architectural style for network application design.

But which one is better? Spoiler alert: it's GraphQL, and here's why ...

REST API Limitations

REST API is a set of rules that enable communication between different software applications over the internet and is widely used for web services and integration purposes. The software offers simplicity, scalability and is platform-independent. However, these advantages come with notable drawbacks. Among these drawbacks are:

Over-fetching and under-fetching data. The over-fetching limitation promotes wasted bandwidth and processing resources, while under-fetching causes additional data requests because all the requested information is unavailable in a single REST endpoint request.

Multiple round trips. Frequently REST APIs require multiple trips to the server to retrieve data. The yo-yo activity increases latency, impacts response times, and hampers the efficiency of content retrieval operations.

Lack of flexibility in data retrieval and manipulation. Traditional APIs usually expose predefined endpoints and fixed data structures, limiting the flexibility for retrieving and manipulating content. This lack of flexibility poses challenges when specific data needs or transformations arise.

Architecture limitations. There are no standards for the implementation of REST APIs. Traditional APIs are often built on monolithic architectures, where all functionalities and data access are tightly coupled. This restricts the ability to scale and evolve individual components independently, hindering agility and innovation.

GraphicQL, The Clear Choice

Compared to REST APIs, GraphQL offers a more efficient approach to specifying and retrieving data without all the wasted network resources and bandwidth. GraphicQL emerges as the clear winner for these reasons:

Efficient and precise queries. The most noticeable difference between GraphQL and REST API is that GraphQL eliminates the problem of over-fetching or under-fetching data. Clients can retrieve multiple resources and their associated fields in a single request.

Strong typing system. GraphQL defines data structures and their relationships better than REST APIs. Using a type system that defines the capabilities of the API, GraphQL allows clients to validate the correctness of their queries, ensures that clients receive the expected data shape, and helps prevent runtime errors.

Single endpoint. GraphQL typically exposes a single endpoint, meaning clients don't need to make multiple requests to different endpoints for data searches. This simplifies the client-side implementation and reduces the number of network requests needed to retrieve data.

Ecosystem and tooling. Because it's an open-source option, GraphQL has a growing ecosystem with various tools and libraries for different programming languages and frameworks. These tools provide developers with helpful features such as code generation, schema stitching, caching, and debugging.

No More Resting On Your Laurels REST API

Enterprise data and its associated relationships are getting more complex every day. The increased complexity means that enterprise content management applications must improve data retrieval, performance and establish a seamless integration with other systems.

GraphQL emerges as a better choice for content management over REST API in a side-by-side comparison. The efficiency, flexibility, and enhanced developer experience provided by GraphQL displays the clear advantages of efficiency, flexibility, and improved developer experience. That's why GraphQL is the recommended development API for IBM FileNet applications. When it comes to harnessing the complete capabilities of content systems, GraphQL gives a competitive edge in the content management landscape.

Brian DeWyer is CTO and Co-Founder of Reveille Software

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"REST" In Peace: Why GraphQL Is Better Then REST API

Brian DeWyer
Reveille Software

Many organizations rely on enterprise content systems to deliver their information efficiently. However, these content services (heavy applications) must integrate with various applications and services to ensure smooth operation. This integration is not an afterthought; it's a critical component for enabling consistency, collaboration, and streamlining the entire data interaction process. And with no-code/low-code themes in Robotic Process Automation (RPA) and Intelligent Automation product sets typically seen in modern content services applications, less is more to accelerate solution delivery.

To accomplish application integrations, IT developers will increasingly have a choice between GraphQL and REST APIs for web client interfaces. Facebook developed GraphQL, now an open-source query language and runtime for APIs. By contrast, Representational State Transfer Application Programming Interface, or REST API, is an architectural style for network application design.

But which one is better? Spoiler alert: it's GraphQL, and here's why ...

REST API Limitations

REST API is a set of rules that enable communication between different software applications over the internet and is widely used for web services and integration purposes. The software offers simplicity, scalability and is platform-independent. However, these advantages come with notable drawbacks. Among these drawbacks are:

Over-fetching and under-fetching data. The over-fetching limitation promotes wasted bandwidth and processing resources, while under-fetching causes additional data requests because all the requested information is unavailable in a single REST endpoint request.

Multiple round trips. Frequently REST APIs require multiple trips to the server to retrieve data. The yo-yo activity increases latency, impacts response times, and hampers the efficiency of content retrieval operations.

Lack of flexibility in data retrieval and manipulation. Traditional APIs usually expose predefined endpoints and fixed data structures, limiting the flexibility for retrieving and manipulating content. This lack of flexibility poses challenges when specific data needs or transformations arise.

Architecture limitations. There are no standards for the implementation of REST APIs. Traditional APIs are often built on monolithic architectures, where all functionalities and data access are tightly coupled. This restricts the ability to scale and evolve individual components independently, hindering agility and innovation.

GraphicQL, The Clear Choice

Compared to REST APIs, GraphQL offers a more efficient approach to specifying and retrieving data without all the wasted network resources and bandwidth. GraphicQL emerges as the clear winner for these reasons:

Efficient and precise queries. The most noticeable difference between GraphQL and REST API is that GraphQL eliminates the problem of over-fetching or under-fetching data. Clients can retrieve multiple resources and their associated fields in a single request.

Strong typing system. GraphQL defines data structures and their relationships better than REST APIs. Using a type system that defines the capabilities of the API, GraphQL allows clients to validate the correctness of their queries, ensures that clients receive the expected data shape, and helps prevent runtime errors.

Single endpoint. GraphQL typically exposes a single endpoint, meaning clients don't need to make multiple requests to different endpoints for data searches. This simplifies the client-side implementation and reduces the number of network requests needed to retrieve data.

Ecosystem and tooling. Because it's an open-source option, GraphQL has a growing ecosystem with various tools and libraries for different programming languages and frameworks. These tools provide developers with helpful features such as code generation, schema stitching, caching, and debugging.

No More Resting On Your Laurels REST API

Enterprise data and its associated relationships are getting more complex every day. The increased complexity means that enterprise content management applications must improve data retrieval, performance and establish a seamless integration with other systems.

GraphQL emerges as a better choice for content management over REST API in a side-by-side comparison. The efficiency, flexibility, and enhanced developer experience provided by GraphQL displays the clear advantages of efficiency, flexibility, and improved developer experience. That's why GraphQL is the recommended development API for IBM FileNet applications. When it comes to harnessing the complete capabilities of content systems, GraphQL gives a competitive edge in the content management landscape.

Brian DeWyer is CTO and Co-Founder of Reveille Software

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Organizations that perform regular audits and assessments of AI system performance and compliance are over three times more likely to achieve high GenAI value than organizations that do not, according to a survey by Gartner ...

Kubernetes has become the backbone of cloud infrastructure, but it's also one of its biggest cost drivers. Recent research shows that 98% of senior IT leaders say Kubernetes now drives cloud spend, yet 91% still can't optimize it effectively. After years of adoption, most organizations have moved past discovery. They know container sprawl, idle resources and reactive scaling inflate costs. What they don't know is how to fix it ...

Artificial intelligence is no longer a future investment. It's already embedded in how we work — whether through copilots in productivity apps, real-time transcription tools in meetings, or machine learning models fueling analytics and personalization. But while enterprise adoption accelerates, there's one critical area many leaders have yet to examine: Can your network actually support AI at the speed your users expect? ...

The more technology businesses invest in, the more potential attack surfaces they have that can be exploited. Without the right continuity plans in place, the disruptions caused by these attacks can bring operations to a standstill and cause irreparable damage to an organization. It's essential to take the time now to ensure your business has the right tools, processes, and recovery initiatives in place to weather any type of IT disaster that comes up. Here are some effective strategies you can follow to achieve this ...

In today's fast-paced AI landscape, CIOs, IT leaders, and engineers are constantly challenged to manage increasingly complex and interconnected systems. The sheer scale and velocity of data generated by modern infrastructure can be overwhelming, making it difficult to maintain uptime, prevent outages, and create a seamless customer experience. This complexity is magnified by the industry's shift towards agentic AI ...

In MEAN TIME TO INSIGHT Episode 19, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA explains the cause of the AWS outage in October ... 

The explosion of generative AI and machine learning capabilities has fundamentally changed the conversation around cloud migration. It's no longer just about modernization or cost savings — it's about being able to compete in a market where AI is rapidly becoming table stakes. Companies that can't quickly spin up AI workloads, feed models with data at scale, or experiment with new capabilities are falling behind faster than ever before. But here's what I'm seeing: many organizations want to capitalize on AI, but they're stuck ...

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