Guest: Tyson Trautmann (LinkedIn)
Company: Fauna (Twitter)
Show: Let’s Talk

One of the problems that application developers experience is spending too much time thinking about their database and as applications mature, it becomes even more time-consuming. Trying to tie the database into DevOps workflows can also be problematic. “Fauna is the ideal operational database for modern application developers. It is a document relational database that is consumed as an API,” says Tyson Trautmann, Vice President of Engineering at Fauna. It stores data on disk as documents but supports the kinds of query patterns and attributes that are typically associated with a relational database.

Existing database offerings can become bottlenecks for developer workflows. The ideal modern database for a developer is serverless, which you consume as an API. Since modern applications run at the edge, there needs to be a consideration for where the data lives as it has a significant impact on performance. Ideal modern databases also handle things like replicating data across regions, making data quickly accessible to consuming applications, and supporting powerful, flexible access patterns.

One of the new features of Fauna is Fauna Schema Language (FSL), which is inspired by a GraphQL schema but is a declarative language for defining your data schema as code in Fauna. However, this is not just a field-level schema for the data itself but defining constraints that need to be true for a transaction to complete, or defining computed fields.

Fauna has also integrated FSL with their Fauna Shell so that developers can define endpoints in any environment to map to where they pull their Fauna schema or where the schema lives. So, this enables them to pull down their schema and FSL files dynamically in their developer workflows. This means you can have a portable way to manipulate schema in any environment and the schema rides along with the code in your repository so that everything natively fits into the developer workflows.

Other Fauna features include backup and copy functionality. Their data import functionality enables developers to validate schema changes against data and consuming applications and validate the transition. This can help developers practice continuous delivery with their data and stop thinking about their data as a snowflake with one-off deployment processes. With Fauna, they can bake these changes directly into their release pipelines and build automation testing those changes so that they can be as fearless in making changes with their data as they are with other software components of the system.

Some of the use cases of Fauna are with customers who have more analytical style queries who will ETL data out of Fauna into an analytical database of their choice. People are also building AI-driven apps on top of Fauna and they are looking at how they can use AI in their products. Other customers are storing their primary data in Fauna and using their integration capabilities to link Fauna to other types of indexes to perform different types of searches over their data.

This summary was written by Emily Nicholls.