Fauna has added an ability to maintain strict enforcement of schema to its document-relational database using a declarative language that makes it simpler to update databases within the context of a DevOps workflow.

While Fauna has enabled IT teams to extend a document database to add tables and rows typically requiring a relational database, Fauna Schema adds the structure, enforcement and reliability of a schema typically associated with traditional relational databases.

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Which of the following best describes how you currently monitor and manage systems across your hybrid cloud environment?(Required)

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Custom built monitoring and management solution

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How valuable would it be for your monitoring and management solution to incorporate predictive analytics to help you identify and address system issues that are critical and not waste time or effort working through all the data and “noise” that is not critical?(Required)

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Moderately valuable

Somewhat valuable

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How do you most typically address potential system issues BEFORE they impact your operations?(Required)

Automated workflows and playbooks to remediate potential issues as soon as they are detected

An orchestrated response process that leverages automation, AI, and human expertise to rapidly respond to potential issues

Advanced monitoring tools such as predictive analytics and anomaly detection to proactively identify and address potential issues

Respond to issues as they arise, using manual processes and troubleshooting techniques

A combination of automated remediation, orchestrated response, and/or proactive monitoring to address potential system issues

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What methods does your organization use to control and monitor cloud/IT spending?(Required)

Centralized procurement processes

Pre-approved budget thresholds

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Department-level spending controls

Regular cost audits and reviews

Cloud cost optimization platform

Approval workflows for resource provisioning

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Which methods do you rely on most to detect security vulnerabilities in your systems?(Required)

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Vendor notifications

Threat modeling (for early detection)

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What are the TWO most common ways you prioritize vulnerabilities to address within your environment?(Required)

Based on potential impact

Based on exploitability

Regulatory compliance requirements

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How do you typically handle system updates and security patches?(Required)

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Fauna CEO Eric Berg said the addition of the approach strengthens the case of using a Fauna database that makes it simpler to streamline the management of databases that can be used to support multiple types of applications. In addition, Fauna now enables database schemas to evolve as the needs of the business evolve using a new Document Types capability that allows developers to define and enforce schema structures directly within the database. Document Types can support both static and dynamic typing, enabling developers to start with a schemaless document database before adding stricter type controls in a way that doesn’t require a database to be taken offline, said Berg.

At the core of that capability is the Fauna Schema Language (FSL), which enables developers to declaratively define their domain models, access controls, and business logic in a human-readable language in a set of files that can be managed alongside their application code.

Using a code repository such as GitHub, DevOps teams can now coordinate schema and application code updates via a continuous integration/continuous delivery (CI/CD) pipeline. Additionally, IT teams can leverage infrastructure-as-code (IaC) tools such as Pulumi and Terraform to automate the deployment and management of their Fauna database configuration.

Fauna also added a Computed Fields capability that allows the values of fields in documents to be dynamically generated based on expressions defined by the user. That approach allows developers to create fields whose values are calculated at the time of query to enable a dynamic composition of objects and field values. These fields can be indexed, providing even greater flexibility in query patterns so developers can accurately define their data model in the database versus requiring them to be defined in mapping layers running outside of the database.

Encoding that database logic within the database allows development teams to define cross-collection joins and subqueries as an element of their schema design to dynamically generate data, and object models in real-time. That capability avoids the performance disadvantages inherent in a data denormalization strategy required by other document databases.

It’s not clear to what degree IT organizations are moving to consolidate databases, but with the rise of document databases, it has become relatively simple for developers to add a database to just about any type of application. The challenge organizations encounter is that as document databases evolve, they become more challenging to manage. It’s not uncommon for developers to pass the responsibility for managing document databases on to a DevOps team that often has a vested interest in consolidation.

After all, the more databases based on disparate architectures there are to manage the more challenging it becomes to manage DevOps workflows at any level of meaningful scale.