"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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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

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For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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