Analyst Perspectives

As I noted in the 2024 Buyers Guide for Operational Data Platforms, intelligent applications powered by artificial intelligence have impacted the requirements for operational data platforms. These applications, infused with contextually relevant recommendations, predictions and forecasting, are driven by machine learning and generative AI. 

Traditionally, operational data platforms support applications used to run the business. Data is then extracted and loaded into analytic data platforms for analysis. The requirement for operational applications to support real-time interactivity and AI changes this dynamic, with the need for analytic processing of data in the operational data platform to deliver predictions and recommendations to accelerate operational decision-making. This has significant implications for emerging operational database providers, such as Fauna, that are positioned specifically to support the development of the next generation of applications. 

Fauna was founded in 2012 by software infrastructure engineers Evan Weaver and Matt Freels to develop the cloud-native transactional database product they would have liked to have had at their disposal in their former roles at what was then known as Twitter (now X). Weaver left Fauna in 2023, but Freels remains with the company as chief architect, leading the continued development of the company’s serverless document-relational database. Fauna raised a $27 million funding round from Madrona Venture Group, Addition Capital, GV and CRV, among others, in 2020, coinciding with the arrival of former Okta chief product officer Eric Berg as CEO and former Snowflake CEO Bob Muglia as chairman. 

Rather than targeting database administrators, Fauna focuses on application developers as the primary users for its managed database service which, it states, has been used to create more than 300,000 databases by over 80,000 development teams in 180 countries. Fauna’s database is typically used to support the development of software-as-a-service applications in industries such as retail and e-commerce, gaming and the Internet of Things. Given the rapid rise of generative AI, Fauna is also increasingly being used to support the development of intelligent operational applications designed to provide integration with large language models and other GenAI application services. 

Fauna describes its product as a document-relational database. As this terminology implies, the product combines the flexibility of the document data model with the consistency and query capabilities associated with relational databases. As I previously described, document databases provide developer agility and application flexibility. Documents can be mapped to objects in application code, providing intuitive ease of use for application developers. The fields used by a document can evolve in response to changing data and application requirements, making document databases suitable for rapid and agile development projects. I assert that by 2027, more than one-half of enterprises will adopt document databases to store data without fixed schema, facilitating rapid application development and business agility. 

Fauna combines the document model with strong consistency thanks to its Distributed Transaction Engine. The engine provides low latency, high availability and atomic, consistent, isolated and durable (or ACID) transactions across geographically distributed regions. I previously explained that the term distributed SQL has been widely adopted to describe operational data platform products that combine the benefits of the relational database model and native support for distributed cloud architecture, including resilience that spans multiple data centers and/or cloud regions. While Fauna delivers the latter, it does not support SQL. However, the Fauna Query Language is a TypeScript-like language that can express declarative relational queries and functional business logic in strongly consistent transactions. FQL also delivers native support for queries requiring joins of data across multiple documents as well as user-defined functions. 

Fauna was developed with native support for event streaming and is delivered as a serverless managed service, which eliminates the need for users to install, configure and manage the database and any associated infrastructure. As I previously explained, although serverless databases are not without challenges, application programming interface-based interactions with serverless databases have the potential to enhance developer productivity and lower the learning curve for developing new, data-driven applications. Fauna also recently delivered Fauna Schema to enable developers to define and manage database schema, including Fauna Schema Language to define and manage database schemas as code with version control, CI/CD pipeline integration and schema enforcement capabilities. Fauna’s API delivery model also lends itself to integration with LLMs and other GenAI cloud services in support of intelligent operational applications. 

The emergence of intelligent applications does not eradicate the use of specialist analytic data platforms, such as data warehouses and data lakehouses. It does, however, impact the requirements for operational data platforms to support real-time analytic functionality for recommendations and predictions. Fauna is positioning its database as a system of record for GenAI applications, storing and processing an enterprise’s user and application data alongside a vector database, which is used to store vector embeddings generated from the enterprise data that can be used to complement and improve trust in GenAI applications. Additionally, the company has delivered its own Fauna AI Assistant, providing a natural language interface to help developers work with FQL via access to documentation, code samples and other content. 

I recommend that enterprises considering data platform providers for the development of next-generation operational applications include Fauna in evaluations. The company has a relatively low profile among emerging database providers, and its lack of support for the SQL standard will give some enterprises pause for thought, but it has already been widely adopted. For developers looking for a combination of agility and transactional consistency, Fauna’s document-relational approach could be ideal. 

Regards,

Matt Aslett