> ## Documentation Index
> Fetch the complete documentation index at: https://docs.solya.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Datasets

> Understand what each analytics dataset is, how it's populated, and what it powers — then browse them in the datasets explorer.

A **dataset** is one analytics table that Solya maintains about your business — your
products, stock, sales, and the metrics derived from them. The **datasets** page lists every
dataset you can access, grouped into six universes, and the **datasets explorer** lets you
open each one in a read-only table view.

## The principle

All datasets are produced by the data platform's ingestion and computation pipelines, then
organized into two analytics layers:

* **Silver** — cleaned, normalized source data: your reference and master entities (products,
  brands, shops…) and conformed taxonomies. This is the trustworthy version of what your POS
  systems sent.
* **Gold** — business-ready analytics built on top of silver: facts (transactions),
  snapshots, per-entity summaries, trends, forecasts, and AI decision data. This is what
  powers dashboards, KPIs, search, alerts, and recommendations.

Two things are true for every dataset:

* **Read-only** — datasets reflect ingested data; you don't edit them here. To change what
  they contain, fix the source data or the [ingestion spec](/en/data-layer/ingestion-spec).
* **Scoped to your organization** — you only ever see your own data.

## Using the explorer

Open any dataset from the datasets page to:

* **Browse** the rows in a table view.
* **Filter** by the dataset's columns.
* **Paginate** through rows and inspect column values.
* **Export** the current view for offline analysis.

## The six universes

The datasets page groups every dataset into six universes. Below is what each one contains
and what it's used for.

### References

Your master and reference entities — the "who and what" of your catalog and network. Built
in the silver layer from your POS data and enriched (taxonomy classification, image scraping)
by the platform.

| Dataset          | What it holds                                              | Powers                                               |
| ---------------- | ---------------------------------------------------------- | ---------------------------------------------------- |
| Products         | Master product records (name, brand, description, images). | Product cards, search, every product-level analysis. |
| Product variants | Variant-level detail (size, color, collection).            | Variant cards, size curves, plan line items.         |
| Inventory items  | The product × shop inventory identity.                     | Stock attribution, per-shop inventory views.         |
| Brands           | Brand master data.                                         | Brand dashboards, brand-level filters.               |
| Shops            | Store / location master data (address, region, country).   | Shop selectors, geographic analysis.                 |
| Suppliers        | Supplier master data.                                      | Procurement, order and supplier-return plans.        |
| Countries        | Country reference data.                                    | Localization, geographic grouping.                   |
| Collections      | Seasonal / product collection definitions.                 | Collection-based filtering and planning.             |

### Taxonomies

Classification hierarchies that organize your catalog (family, gender, color, size). Derived
during ingestion, partly via AI classification.

| Dataset          | What it holds                                  | Powers                                    |
| ---------------- | ---------------------------------------------- | ----------------------------------------- |
| Taxonomy         | The global classification hierarchy.           | Consistent categorization across the app. |
| Brand taxonomy   | Brand-level classification.                    | Brand grouping and navigation.            |
| Product taxonomy | Product-level classification (family, gender). | Category filters and reporting.           |
| Variant taxonomy | Variant-level classification (color, size).    | Size curves, color/size breakdowns.       |

### Transactions (facts)

The event-level record of what happened — denormalized star-schema fact tables in the gold
layer, built from your sales, orders, and movements.

| Dataset        | What it holds                                                          | Powers                                                |
| -------------- | ---------------------------------------------------------------------- | ----------------------------------------------------- |
| Sales lines    | Individual sale line items (quantity, price, cost, margin, discounts). | Sales, margin and discount analysis; KPIs; forecasts. |
| Order lines    | Purchase / order line items (quantity, cost, supplier).                | Procurement analysis, supplier performance.           |
| Movement lines | Stock movements (receipts, transfers, adjustments).                    | Stock-flow analysis, transfer tracking.               |

### Stock

The state of your inventory, both historically and right now.

| Dataset        | What it holds                                              | Powers                                                 |
| -------------- | ---------------------------------------------------------- | ------------------------------------------------------ |
| Stock ledger   | Historical stock movements with running balances and cost. | Stock aging, historical reconstruction, cost of stock. |
| Stock snapshot | Current stock by shop × variant, with days-of-supply.      | Stockout-risk alerts, reorder points, stock valuation. |

### Summaries

Pre-aggregated per-entity metrics so common questions answer instantly without scanning the
facts.

| Dataset         | What it holds                                       | Powers                             |
| --------------- | --------------------------------------------------- | ---------------------------------- |
| Product summary | Per-product aggregates (sales, stock, performance). | Product cards and dashboards.      |
| Brand summary   | Per-brand aggregates.                               | Brand portfolio analysis.          |
| Shop summary    | Per-shop aggregates.                                | Store performance and comparisons. |
| Variant summary | Per-variant aggregates.                             | Variant cards and recommendations. |

### Analytics

Pre-computed trends and entity × shop metrics that feed visualizations and risk detection.

| Dataset                | What it holds                                             | Powers                                         |
| ---------------------- | --------------------------------------------------------- | ---------------------------------------------- |
| Sales trends           | Daily sales aggregates with rolling windows (7d/30d/90d). | Trend charts, demand sensing.                  |
| Stock trends           | Historical stock levels by time window.                   | Stock trend charts, stock-out risk.            |
| Product-shop analytics | Pre-aggregated product × shop metrics.                    | Store allocation, product-by-shop performance. |
| Brand-shop analytics   | Pre-aggregated brand × shop metrics.                      | Brand performance per store.                   |

<Note>
  For the full technical reference — every table and column, including the gold-only AI tables
  (forecasts, decision vector) — see the [data catalog](/en/developers/data-catalog). For the
  operational, app-managed data you create (plans, rules, alerts, tags…), see the
  [data model](/en/developers/data-model).
</Note>