DDAG — Dynamic Database API Gateway

The description states that DDAG is a self-hosted database API gateway. It turns parameterized SQL into managed REST APIs, with support for:

• OAuth2 and JWT validation
• Role-based access control (RBAC)
• Query timeouts, row limits, and safe parameter binding
• Caching and connection pooling
• Audit trails and Prometheus metrics
• Automatic OpenAPI documentation generation

It supports PostgreSQL as its primary database and uses a shared connector architecture for MySQL/MariaDB, Oracle, and SQL Server.

Inference The product appears to be a middleware or gateway that abstracts database interactions into HTTP endpoints while enforcing access control and operational policies. It is not a database itself but a tool for publishing database operations as APIs.

The author positions DDAG as a solution to the problem of repeated engineering work when exposing databases via APIs. The claim evolution shows:

• Initial framing: Teams often build separate backend services for each database endpoint.
• Problem identification: This leads to scattered credentials, policies, and audit records.
• Solution proposition: A single platform that governs database API publishing without requiring new backend code.
• Value proposition: Consistent, governed way to publish database operations as secure APIs.

Inference The positioning suggests DDAG is aimed at internal teams or developers who need to quickly expose database data for automation, reporting, or partner access — not end-users or public-facing APIs.

The description states that DDAG targets teams needing to expose database data to:

• Internal applications
• Automation tools
• Partners
• Reporting systems

It is described as a tool for administrators who define database connections, SQL templates, and access policies.

Inference The primary customer segment likely includes DevOps engineers, platform engineers, or internal developers working in organizations with complex database infrastructures. The ICP seems to be teams that want to reduce API development overhead while maintaining governance.

There is no evidence of pricing, licensing, or monetization strategy in the description.

Not evidenced.

The project is built using:

• Backend: Go
• Frontend: Vue 3 + Vite
• Database: PostgreSQL (for metadata), Redis (caching/runtime coordination)
• Deployment: Docker Compose, containerized for server environments
• Technology stack includes: OAuth2, JWT, gRPC, Kubernetes, OpenAPI/Swagger, Prometheus

It supports multiple SQL dialects via a shared connector architecture and implements:

• Control-plane and data-plane separation
• Safe parameter binding
• Caching logic based on operation type
• Structured logging and request correlation
• Audit trails and metrics collection

Inference The technical design shows an emphasis on modularity, security, and observability. The use of Go and containerization suggests a focus on performance and deployment flexibility.

There is no evidence of revenue, customers, or adoption beyond the hackathon submission.

Not evidenced.

The description does not mention competitors directly. However, based on its functionality — turning databases into secure APIs with governance — it likely competes with:

• API gateways (e.g., Kong, Apigee)
• Database abstraction layers or middleware
• Internal developer platforms that provide API scaffolding

It also overlaps conceptually with tools like PostgREST or Hasura, which also expose databases as REST APIs.

Inference DDAG appears to be a niche solution focused on self-hosted, governed access to SQL databases. Its competitive edge may lie in its multi-database support and fine-grained policy enforcement.

• No traction or revenue evidence: The project is only described as a hackathon submission.
• Limited scope: Only one team member (Heri Riski A) is listed, raising questions about scalability or long-term maintenance.
• Self-hosted model: May limit adoption in enterprise settings where managed services are preferred.
• Security complexity: Implementing RBAC, JWT validation, audit trails, and rate limiting across multiple databases adds risk of misconfiguration or vulnerabilities.
• Lack of testing for other DBs: While MySQL/Oracle support is mentioned, live integration testing is listed as a future priority — suggesting early-stage maturity.

1. What is the current status of MySQL, Oracle, and SQL Server connector implementations?
2. How does DDAG handle schema changes or migration in the underlying databases?
3. Are there any plans for managed cloud offerings or SaaS versions?
4. Has the team considered integrating with existing identity providers (e.g., Okta, Auth0)?
5. What are the performance benchmarks for typical query loads?
6. How is data encryption handled at rest and in transit?
7. Is there a roadmap for supporting non-SQL databases (e.g., NoSQL)?