---
title: "Overview"
source: "https://docs.vertesiahq.com/data-platform/overview"
markdown: "https://docs.vertesiahq.com/llms/data-platform/overview.md"
---

# Overview

The **Data Platform** is Vertesia's solution for unified data management, combining AI-powered schema creation, SQL analytics, and interactive dashboards in one integrated experience.

## What is the Data Platform?

The Data Platform enables you to:

- **Manage structured data** using DuckDB-backed DataStores
- **Import data** from CSV, JSON, Parquet, and Excel files
- **Query data** using standard SQL with DuckDB extensions
- **Create dashboards** with Vega-Lite visualizations
- **Analyze data** with AI assistance through specialized agents

## Key Concepts

### DataStores

A DataStore is a DuckDB database that stores your structured data. Each DataStore:

- Contains one or more tables with defined schemas
- Supports SQL queries with DuckDB's powerful analytics extensions
- Provides automatic versioning for schema changes and data imports
- Can be linked to a Collection for file-based data sources

### Tables

Tables within a DataStore have:

- **Columns** with types: STRING, INTEGER, BIGINT, FLOAT, DOUBLE, DECIMAL, BOOLEAN, DATE, TIMESTAMP, JSON
- **Semantic types** for enhanced understanding: email, phone, url, currency, percentage, person_name, address, country, date_iso, identifier
- **Foreign key relationships** with referential integrity
- **Indexes** for query optimization

### Dashboards

Dashboards are Vega-Lite visualizations backed by SQL queries:

- **Single or multi-panel** layouts with vconcat, hconcat, or grid arrangements
- **Interactive selections** for cross-filtering between panels
- **Query parameters** with `{{param}}` syntax for dynamic filtering
- **Automatic versioning** with named snapshots for important states

### Projects

A Project links a Collection (files) with a DataStore (database):

- Files in the Collection serve as data sources
- The DataStore holds the structured, queryable data
- Linked via `dp:<project-slug>` tag
- AI agents can analyze files and automatically create schemas

## Architecture

```
Collection (files)     DataStore (DuckDB)
      │                      │
      └──── dp:<name> tag ───┘
            │
      Project linking
```

- **Tag-based linking**: Collections and DataStores are connected through tags
- **GCS storage**: DataStore files are stored in Google Cloud Storage
- **Versioning**: Automatic versions created on schema changes and imports
- **Snapshots**: Named snapshots protected from automatic cleanup

## Use Cases

### Business Intelligence Dashboards

Create interactive dashboards that visualize key metrics from your data. Combine bar charts, line graphs, and tables to tell a data story.

### Data Analysis with AI

Use AI agents to explore your data, run complex queries, and generate insights. The AI can understand your schema and write appropriate SQL queries.

### ETL and Data Import

Import data from various sources (CSV, JSON, Parquet) with atomic operations. Transform and clean data during import with column mapping and type conversion.

### Schema Design

Let AI analyze your data files and suggest optimal database schemas with appropriate types, relationships, and indexes.

## Next Steps

- [Getting Started](/data-platform/getting-started) - Create your first DataStore and dashboard
- [Tools Reference](/data-platform/tools) - Complete reference for Data Platform tools
- [Skills Reference](/data-platform/skills) - Learn about data-focused agent skills
- [Dashboards](/data-platform/dashboards) - Deep dive into Vega-Lite visualizations