This repository is intended to evolve into a production-ready, self-hosted data platform distribution for CloudStack-based environments. The long-term goal is not only to provide modular local development stacks, but also to offer a deployable platform foundation that can reduce or replace a large portion of bespoke platform engineering effort.
The architecture is therefore designed around two parallel needs:
- modularity, so components can be swapped or upgraded over time
- operational rigor, so the resulting platform can mature toward production use
The platform is organized around:
- modules: reusable platform components
- profiles: runnable combinations of modules for specific deployment targets
- shared tooling: common scripts, templates, validation, and conventions
- examples: sample workloads used for testing and demonstration
This structure allows the platform to support different combinations of orchestration, storage, transformation, validation, and BI technologies without rewriting the entire repository.
The platform is being designed to support the following outcomes over time:
- local development and experimentation
- reproducible integration environments
- opinionated deployment profiles
- production-oriented stack distributions
- deployment into CloudStack-backed infrastructure
- a reduction in one-off platform glue and custom operational work
In other words, this repository is not just a demo stack. It is intended to become a maintainable platform foundation.
The platform is divided into logical layers.
| Layer | Responsibility | Example modules |
|---|---|---|
| Secrets | credentials, secret injection, key management | Vault |
| Infrastructure services | service coordination backends and support systems | KeyDB, nginx |
| Storage / compute | databases, warehouses, and processing engines | Postgres, MariaDB, Spark |
| Orchestration | workflow scheduling and task execution | Airflow, Dagster |
| Transformation | data modeling and transformation | dbt |
| Quality | data validation and testing | Great Expectations, Soda |
| BI / access | dashboards, semantic access, and exploration | Superset, Metabase |
| Platform tooling | bootstrap, diagnostics, deployment helpers | shared scripts, templates |
Not every profile will contain every layer, but this layered model makes module responsibilities explicit and supports future production hardening.
A module represents one implementation of a platform capability.
Examples:
modules/orchestration/airflowmodules/orchestration/dagstermodules/warehouse/postgresmodules/warehouse/mariadbmodules/warehouse/sparkmodules/transform/dbtmodules/quality/great-expectationsmodules/bi/supersetmodules/secrets/vault
Each module owns:
- its service definitions
- its configuration
- its runtime assumptions
- its module-specific documentation
This makes it possible to evolve the platform one capability at a time.
A profile is a supported platform composition.
Profiles define:
- which modules are included
- how modules are connected
- environment conventions
- networking and storage assumptions
- startup and deployment flow
- the intended use case
Examples of future profile types:
- local development
- single-node integration
- production baseline
- CloudStack deployment
- high-availability variants
A profile is the unit that should eventually become installable, testable, and supportable.
Because the target environment is CloudStack, the architecture should avoid assumptions that only fit managed cloud services. Instead, it should favor:
- self-hosted components
- explicit networking and storage configuration
- container-first deployment patterns
- infrastructure that can run on VMs or Kubernetes
- clear bootstrap and operational procedures
- replaceable dependencies
This makes the platform more suitable for private-cloud or infrastructure-managed environments where more operational responsibility remains within the platform itself.
Even when a module starts as a local development component, it should be designed with a path toward production deployment in mind.
Each module should have one primary responsibility and avoid hidden coupling to unrelated components.
Platform capabilities should not be tied to one implementation forever.
Examples:
- Postgres may later be replaced or complemented by MariaDB or Spark-based storage
- Airflow may be replaced by Dagster for some profiles
- Great Expectations may be replaced by another validation framework
Supported stack combinations should be explicit. The repository should avoid an uncontrolled mix-and-match model and instead define supported profiles with known compatibility.
All runtime assumptions should be documented:
- networks
- secrets
- storage
- ports
- bootstrap steps
- health checks
- backup expectations
- upgrade paths
The platform should be runnable across:
- local development environments
- single-node servers
- multi-VM CloudStack environments
- container orchestration layers where appropriate
The platform will likely mature in stages.
- module boundaries defined
- one or more profiles runnable locally
- example workloads validate behavior
- deterministic startup
- documented bootstrap
- basic health checks and validation
- simplified operator workflows
- hardened secrets handling
- backup and restore procedures
- monitoring and logging standards
- upgrade procedures
- failure recovery expectations
- documented security posture
- deployment patterns aligned to CloudStack
- VM/container layout guidance
- environment-specific profiles
- operational runbooks
- supportable release model
- implementation-specific service definitions
- module-specific configuration
- module documentation
- module-local scripts
- implementation details
- supported combinations of modules
- deployment target assumptions
- shared environment conventions
- topology and runtime wiring
- operator-facing setup and operations documentation
- bootstrap helpers
- diagnostics
- validation tooling
- common templates
- cross-profile conventions
For this project, production readiness should eventually include:
- documented supported profiles
- deterministic installation and upgrade paths
- secrets handling that does not rely on committed credentials
- backup and restore support
- health checks and service readiness guarantees
- logging and monitoring integration points
- security hardening guidance
- version compatibility documentation
- operator documentation and runbooks
This repository is intended to become more than a modular demo stack. It is being structured as the foundation for a production-ready, self-hosted data platform distribution suitable for CloudStack environments.
To support that goal, the architecture separates:
- modules for replaceable implementations
- profiles for supported platform distributions
- shared tooling for consistent operations
- examples for validation and testing
This structure allows the platform to grow from local development into a production-capable distribution without locking the project into one fixed stack too early.