# Analytics-as-Code

> Analytics-as-code is the durability backbone behind Holistics' expressive semantic layer. Every definition is code, version-controlled, and reviewable, which unlocks governance, composability, developer ergonomics, AI extensibility, and automation.

Analytics-as-code is the **durability backbone** behind Holistics' programmable semantic layer ([AML](/reference/aml/)) and composable query language ([AQL](/as-code/aql/)). The semantic layer is what lets AI and humans reason from real business definitions; analytics-as-code is what keeps those definitions durable, evolvable, and useful inside modern engineering workflows.

For the full positioning argument, see [Why Holistics](/docs/difference).

## What "analytics-as-code" means here

Every definition in Holistics is **code, in a Git repository**:

- **Models, datasets, dashboards, relationships**: written in [AML](/reference/aml/), the typed declarative language
- **Metrics**: written in [AQL](/as-code/aql/), composable and reusable
- **Permissions, user attributes, environments**: also defined declaratively in code

You can edit through the GUI or write code directly in [AML Studio](/docs/development/aml-studio). Both write to the same underlying code base. Two-way sync means data teams and analytics engineers don't have to choose one workflow over the other.

## What treating analytics as code unlocks

Many BI tools offer "Git export" or YAML modeling as a developer-experience add-on. In Holistics, code is the substrate, which means several capabilities follow at once:

- **Governance.** Every definition has history, authorship, review, branches, environments, and rollback. The same engineering rigor that protects production code now protects business logic. ([See below](#governance-durable-reviewable-traceable-business-logic).)
- **Composability and reuse.** Many BI tools call themselves "analytics-as-code" but use YAML, which is schemaless, untyped, and forces Jinja-template workarounds for any reuse. [AML](/reference/aml/) is a typed language built for analytics: real types, real abstractions ([modules](/reference/aml/module), [extends](/reference/aml/extend), [partials](/reference/aml/partial), [constants](/reference/aml/constant), [string interpolation](/reference/aml/string-interpolation), [conditionals](/reference/aml/if-else)), and real IDE tooling. See [AML vs YAML](/as-code/amql/aml-vs-yaml) for the full argument.
- **Developer ergonomics.** Type checking at compile time, autocomplete and go-to-definition in [AML Studio](/docs/development/aml-studio), [inspectable compiled SQL](/as-code/aql/), and a [CLI](/docs/cli/) for local development against the editor of your choice.
- **AI extensibility.** AI agents can read, write, and edit structured code; that's far harder against opaque GUI configurations. Holistics AI generates AQL against the same code your team wrote, and external tools can drive the system through the [MCP server](/docs/ai/connect-external-tools/mcp-server) or [API](/api/).
- **Automation.** [CI/CD validation](/docs/continuous-integration/), [auto-publish on merge](/docs/continuous-integration/auto-publish), programmatic deploys, and [dynamic environments per branch](/docs/continuous-integration/dynamic-dev-prod-environment).

## Governance: durable, reviewable, traceable business logic

Governance is the most load-bearing benefit of analytics-as-code in a BI context, because business definitions drift faster than application code and the consequences of drift are silent. Code-as-substrate makes drift visible and reversible.

| Capability | What it enables |
|---|---|
| **History & authorship** | Every change has an author, a timestamp, and a diff. Disputes about "who changed the revenue definition?" are answered in seconds. |
| **Pull request review** | Wrong logic doesn't get merged silently. The same review discipline that protects production code now protects business definitions. |
| **Branches** | Experiment in isolation. Test a new metric definition on real data without affecting production users. |
| **Environments** | Develop in dev, validate in staging, deploy to prod through a real promotion workflow. |
| **CI/CD validation** | AML compiles. Type-check business logic before merge. Catch breaks before users do. |
| **Inspectable compiled SQL** | AQL compiles deterministically to SQL, and the output is visible. Trust in human-written *and* AI-written queries comes from being able to see exactly what runs. |
| **Rollback** | Reverting a problematic change is one Git operation, not a forensic exercise. |

The cumulative effect: the semantic layer gets **stronger over time** rather than drifting. Business logic improves through review. Metric definitions stay consistent across human and AI consumers. Embedded customer-facing analytics share the same governed substrate as internal dashboards.

## How AI benefits from this

Holistics AI generates [AQL](/as-code/aql/), not raw SQL, against your governed semantic layer. That's only meaningful if the semantic layer is actually governed. Analytics-as-code is what makes "governed" real:

- AI reuses metric definitions you've **reviewed and merged**, not ad-hoc fragments
- AI's queries compile to inspectable SQL you can verify
- When you improve a metric definition through a PR, every AI answer that uses that metric improves with it instantly, without retraining
- Permission checks happen on the same code-defined access controls humans use

See [Why Holistics AI is reliable](/docs/ai/architecture) for the mechanism in detail.

## Where to go from here

### The basics
- [Version Control with Git](/docs/git-version-control/): branches, history, rollback
- [Quickstart](/docs/quickstart): end-to-end: connect, model, develop, publish
- [Code Deployment (Dev/Prod)](/docs/development/dev-prod-mode): how dev and prod separation works

### Code review
- [Pull Request Workflow](/docs/continuous-integration/pr-workflow-auto-deploy): the PR loop in Holistics
- [GitHub PR Workflow](/docs/continuous-integration/github-pr-workflow): connect with GitHub
- [GitLab MR Workflow](/docs/continuous-integration/gitlab-mr-workflow): connect with GitLab
- [Code Search](/docs/development/code-search): search across your analytics code base

### CI/CD
- [CI/CD Overview](/docs/continuous-integration/): automated validation and deployment
- [Validation API](/docs/continuous-integration/validation-api): validate AML changes in your CI pipeline
- [Auto-publish](/docs/continuous-integration/auto-publish): automatic deployment after merge

### Multi-environment
- [Dynamic Data Sources](/docs/development/dynamic-data-source): different warehouses per environment
- [Dynamic Schemas](/docs/development/dynamic-schema): different schemas per environment
- [Dynamic Environments](/docs/continuous-integration/dynamic-dev-prod-environment): full env promotion patterns

### Integrations
- [dbt Integration](/docs/dbt-integration/): work alongside dbt for transformation
- [Local Agentic Dev](/docs/development/local-agentic-development): work in your editor (or with a coding agent) using the Holistics CLI