GRAPH QL. Your best ally for the creation of data products.

The era of digitalisation in which we find ourselves has filled our daily lives with data products or data-driven products. In this post we discover what they are and show you one of the key data technologies to design and build this kind of products: GraphQL.

Introduction

Let's start at the beginning, what is a data product? A data product is a digital container (a piece of software) that includes data, metadata and certain functional logics (what and how I handle the data). The aim of such products is to facilitate users' interaction with a set of data. Some examples are:

• Sales scorecard: Online businesses have tools to track their sales performance, with graphs showing trends and rankings, to assist in decision making.
• Apps for recommendations:  Streaming TV services have functionalities that show content recommendations based on the user's historical tastes.
• Mobility apps. The mobile apps of new mobility services (such as Cabify, Uber, Bolt, etc.) combine user and driver data and metadata with predictive algorithms, such as dynamic fare calculation or optimal driver assignment, in order to offer a unique user experience.
• Health apps: These applications make massive use of data captured by technological gadgets (such as the device itself, smart watches, etc.) that can be integrated with other external data such as clinical records and diagnostic tests.
• Environmental monitoring: There are apps that capture and combine data from weather forecasting services, air quality systems, real-time traffic information, etc. to issue personalised recommendations to users (e.g. the best time to schedule a training session, enjoy the outdoors or travel by car).

As we can see, data products accompany us on a daily basis, without many users even realising it. But how do you capture this vast amount of heterogeneous information from different technological systems and combine it to provide interfaces and interaction paths to the end user? This is where GraphQL positions itself as a key technology to accelerate the creation of data products, while greatly improving their flexibility and adaptability to new functionalities desired by users.

What is GraphQL?          

GraphQL saw the light of day on Facebook in 2012 and was released as Open Source in 2015. It can be defined as a language and an interpreter of that language, so that a developer of data products can invent a way to describe his product based on a model (a data structure) that makes use of the data available through APIs.

Before the advent of GraphQL, we had (and still have) the technology REST, which uses the HTTPs protocol  to ask questions and get answers based on the data. In 2021, we introduced a post where we presented the technology and made a small demonstrative example of how it works. In it, we explain REST API as the standard technology that supports access to data by computer programs. We also highlight how REST is a technology fundamentally designed to integrate services (such as an authentication or login service).

In a simple way, we can use the following analogy. It is as if REST is the mechanism that gives us access to a complete dictionary. That is, if we need to look up any word, we have a method of accessing the dictionary, which is alphabetical search. It is a general mechanism for finding any available word in the dictionary. However, GraphQL allows us, beforehand, to create a dictionary model for our use case (known as a "data model"). So, for example, if our final application is a recipe book, what we do is select a subset of words from the dictionary that are related to recipes.

To use GraphQL, data must always be available via an API. GraphQL provides a complete and understandable description of the API data, giving clients (human or application) the possibility to request exactly what they need.       As quoted in this post, GraphQL is like an API to which we add a SQL-style "Where" statement.

Below, we take a closer look at GraphQL's strengths when the focus is on the development of data products.

Benefits of using GraphQL in data products:

• With GraphQL, the amount of data and queries on the APIs is considerably optimised . APIs for accessing certain data are not intended for a specific product (or use case) but as a general access specification (see dictionary example above). This means that, on many occasions, in order to access a subset of the data available in an API, we have to perform several chained queries, discarding most of the information along the way. GraphQL optimises this process, as it defines a predefined (but adaptable in the future) consumption model over a technical API. Reducing the amount of data requested has a positive impact on the rationalisation of computing resources, such as bandwidth or caches, and improves the speed of response of systems.
• This has an immediate effect on the standardisation of data access. The model defined thanks to GraphQL creates a data consumption standard for a family of use cases. Again, in the context of a social network, if what we want is to identify connections between people, we are not interested in a general mechanism of access to all the people in the network, but a mechanism that allows us to indicate those people with whom I have some kind of connection. This kind of data access filter can be pre-configured thanks to GraphQL.
• Improved safety and performance: By precisely defining queries and limiting access to sensitive data, GraphQL can contribute to a more secure and better performing application.

Thanks to these advantages, the use of this language represents a significant evolution in the way of interacting with data in web and mobile applications, offering clear advantages over more traditional approaches such as REST.

Generative Artificial Intelligence. A new superhero in town.

If the use of GraphQL language to access data in a much more efficient and standard way is a significant evolution for data products, what will happen if we can interact with our product in natural language? This is now possible thanks to the explosive evolution in the last 24 months of LLMs (Large Language Models) and generative AI.

The following image shows the conceptual scheme of a data product, intLegrated with LLMS: a digital container that includes data, metadata and logical functions that are expressed as functionalities for the user, together with the latest technologies to expose information in a flexible way, such as GraphQL and conversational interfaces built on top of Large Language Models (LLMs).

How can data products benefit from the combination of GraphQL and the use of LLMs?

• Improved user experience. By integrating LLMs, people can ask questions to data products using natural language, . This represents a significant change in how we interact with data, making the process more accessible and less technical. In a practical way, we will replace the clicks with phrases when ordering a taxi.
• Security improvements along the interaction chain in the use of a data product. For this interaction to be possible, a mechanism is needed that effectively connects the backend (where the data resides) with the frontend (where the questions are asked). GraphQL is presented as the ideal solution due to its flexibility and ability to adapt to the changing needs of users,offering a direct and secure link between data and questions asked in natural language. That is, GraphQl can pre-select the data to be displayed in a query, thus preventing the general query from making some private or unnecessary data visible for a particular application.
• Empowering queries with Artificial Intelligence: The artificial intelligence not only plays a role in natural language interaction with the user. One can think of scenarios where the very model that is defined with GraphQL is assisted by artificial intelligence itself. This would enrich interactions with data products, allowing a deeper understanding and richer exploration of the information available. For example, we can ask a generative AI (such as ChatGPT) to take this catalogue data that is exposed as an API and create a GraphQL model and endpoint for us.

In short, the combination of GraphQL and LLMs represents a real evolution in the way we access data. GraphQL's integration with LLMs points to a future where access to data can be both accurate and intuitive, marking a move towards more integrated information systems that are accessible to all and highly reconfigurable for different use cases. This approach opens the door to a more human and natural interaction with information technologies, aligning artificial intelligence with our everyday experiences of communicating using data products in our day-to-day lives.

Content prepared by Alejandro Alija, expert in Digital Transformation and Innovation.

The contents and points of view reflected in this publication are the sole responsibility of its author.