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Sample data sets in Cosmos DB in Microsoft Fabric

  • Applies to: ✅ Cosmos DB in Microsoft Fabric

Microsoft Fabric's Cosmos DB database workload provides built-in sample data sets to help you explore, learn, and experiment with NoSQL database patterns. This data set represents an e-commerce scenario with products and customer reviews, demonstrating how different entity types coexist in the same container.

Two sample data sets are available:

  • Standard sample data: Core e-commerce data with products and reviews
  • Vector sample data: Enhanced version that includes 1536-dimensional vector embeddings generated using OpenAI's text-embedding-ada-002 model for semantic search scenarios.

Data set overview

Both sample data sets contain the same e-commerce data with two document types.

  • Product documents (docType: "product") - Individual products with name, description, inventory, current price, and an embedded array of the price history for that product.
  • Review documents (docType: "review") - Customer reviews and ratings linked to products via productId

The vector sample data set is based on the standard sample data set. Product documents in the vector data set include an additional vectors property containing 1536-dimensional embeddings for semantic search capabilities.

Note

You can find both datasets as well as an additional dataset with vectors generated using the OpenAI text-embedding-3-large model with 512 dimensions in the Sample Datasets folder of the Cosmos DB in Fabric - Samples Repository

Document schemas

Product document schema

Product documents contain detailed information about individual items in the e-commerce catalog:

Property Type Description
id string Unique identifier for the product in GUID format
docType string Document type identifier, always "product"
productId string Product identifier, same as id for product documents
name string Product display name
description string Detailed product description
categoryName string Product category (e.g., "Computers, Laptops", "Media", "Accessories")
inventory number Number of items currently in stock
firstAvailable string Date when product became available (ISO 8601 format)
currentPrice number Current selling price
priceHistory array Array of price change objects with date and price fields
priceHistory[].date string Date and time of the price change in ISO 8601 format
priceHistory[].price number Price at the specified date
vectors array Vector sample data only - 1536-dimensional vector embedding

Review document schema

Review documents contain customer feedback and ratings for products:

Property Type Description
id string Unique identifier for the review in GUID format
docType string Document type identifier, always "review"
productId string References the id of the product being reviewed
categoryName string Product category (inherited from the reviewed product)
customerName string Name of the customer who wrote the review
reviewDate string Date when the review was submitted (ISO 8601 format)
stars number Rating given by the customer (1-5 scale)
reviewText string Written review content from the customer

Note

Cosmos DB automatically generates system properties (_rid, _self, _etag, _attachments, _ts) for all documents.

Note

For more information about the ISO 8601 format, see international date and time standard. For more information about the GUID format, see universally unique identifiers.

Example documents

The following examples show the structure of documents in both sample data sets.

Standard product document example

{
  "id": "ae449848-3f15-4147-8eee-fe76cfcc6bb4",
  "docType": "product",
  "productId": "ae449848-3f15-4147-8eee-fe76cfcc6bb4",
  "name": "EchoSphere Pro ANC-X900 Premium Headphones",
  "description": "EchoSphere Pro ANC-X900 Premium Headphones deliver immersive sound with advanced 40mm drivers and Adaptive Hybrid Active Noise Cancellation. Bluetooth 5.3 ensures seamless connectivity.",
  "categoryName": "Accessories, Premium Headphones",
  "inventory": 772,
  "firstAvailable": "2024-01-01T00:00:00",
  "currentPrice": 454.87,
  "priceHistory": [
    {
      "date": "2024-01-01T00:00:00",
      "price": 349.0
    },
    {
      "date": "2024-08-01T00:00:00",
      "price": 363.0
    },
    {
      "date": "2025-04-01T00:00:00",
      "price": 408.14
    },
    {
      "date": "2025-08-01T00:00:00",
      "price": 454.87
    }
  ]
}

Vectorized product document example

{
    "id": "ae449848-3f15-4147-8eee-fe76cfcc6bb4",
    "docType": "product",
    "productId": "ae449848-3f15-4147-8eee-fe76cfcc6bb4",
    "name": "EchoSphere Pro ANC-X900 Premium Headphones",
    "description": "EchoSphere Pro ANC-X900 Premium Headphones deliver immersive sound with advanced 40mm drivers and Adaptive Hybrid Active Noise Cancellation. Bluetooth 5.3 ensures seamless connectivity.",
    "categoryName": "Accessories, Premium Headphones",
    "inventory": 772,
    "firstAvailable": "2024-01-01T00:00:00",
    "currentPrice": 454.87,
    "priceHistory": [
      {
        "date": "2024-01-01T00:00:00",
        "price": 349.0
      },
      {
        "date": "2025-08-01T00:00:00",
        "price": 454.87
      }
    ],
    "vectors": [
      -0.02783808670938015,
      0.011827611364424229,
      -0.04711977392435074,
      // ... (1536 dimensions total)
      0.04251981899142265
    ]
}

Review document example

Review documents are identical in both sample data sets:

{
  "id": "fa799013-1746-4a7f-bd0f-2a95b2b76481",
  "docType": "review",
  "productId": "e847e069-d0f9-4fec-b42a-d37cd5b2f536",
  "categoryName": "Accessories, Premium Headphones",
  "customerName": "Emily Rodriguez",
  "reviewDate": "2025-03-02T00:00:00",
  "stars": 5,
  "reviewText": "Excellent sound quality! Premium build! This EchoSphere Pro ANC-X900 exceeded hopes."
}

How to use the sample data

Both sample data sets help you practice querying, filtering, and aggregating data in Cosmos DB. The mixed document types provide realistic scenarios for various use cases.

Standard sample data scenarios

  • Joining related data: Link reviews to products using productId
  • Category analysis: Query products and reviews by categoryName
  • Review analysis: Examine customer feedback patterns and ratings

Common query patterns

Get all products in a category:

SELECT *
FROM c
WHERE 
  c.docType = "product" AND 
  c.categoryName = "Computers, Laptops"

Get reviews for a specific product:

SELECT *
FROM c
WHERE 
  c.docType = "review" AND 
  c.productId = "77be013f-4036-4311-9b5a-dab0c3d022be"

Vector sample data scenarios

  • Semantic similarity search: Find products with similar features using vector embeddings
  • Content-based recommendations: Generate product suggestions based on description similarity
  • Hybrid queries: Combine traditional filters with vector similarity for enhanced results

JSON schemas

The following JSON schemas describe the structure of documents in both sample data sets. Use these schemas to validate or generate similar data for your own Cosmos DB workloads.

Standard product document schema

{
  "type": "object",
  "properties": {
    "id": { "type": "string" },
    "docType": { "type": "string" },
    "productId": { "type": "string" },
    "name": { "type": "string" },
    "description": { "type": "string" },
    "categoryName": { "type": "string" },
    "inventory": { "type": "number" },
    "firstAvailable": { "type": "string" },
    "currentPrice": { "type": "number" },
    "priceHistory": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "date": { "type": "string" },
          "price": { "type": "number" }
        },
        "required": ["date", "price"]
      }
    }
  },
  "required": [
    "id", "docType", "productId", "name", "description", "categoryName", "inventory", "firstAvailable", "currentPrice", "priceHistory"
  ]
}

Vector-enabled product document schema

{
  "type": "object",
  "properties": {
    "id": { "type": "string" },
    "docType": { "type": "string" },
    "productId": { "type": "string" },
    "name": { "type": "string" },
    "description": { "type": "string" },
    "categoryName": { "type": "string" },
    "inventory": { "type": "number" },
    "firstAvailable": { "type": "string" },
    "currentPrice": { "type": "number" },
    "priceHistory": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "date": { "type": "string" },
          "price": { "type": "number" }
        },
        "required": ["date", "price"]
      }
    },
    "vectors": {
      "type": "array",
      "items": { "type": "number" },
      "minItems": 1536,
      "maxItems": 1536
    }
  },
  "required": [
    "id", "docType", "productId", "name", "description", "categoryName", "inventory", "firstAvailable", "currentPrice", "priceHistory", "vectors"
  ]
}

Review document schema

{
  "type": "object",
  "properties": {
    "id": { "type": "string" },
    "docType": { "type": "string", "const": "review" },
    "productId": { "type": "string" },
    "categoryName": { "type": "string" },
    "customerName": { "type": "string" },
    "reviewDate": { "type": "string" },
    "stars": { "type": "number" },
    "reviewText": { "type": "string" }
  },
  "required": [
    "id", "docType", "productId", "categoryName", "customerName", 
    "reviewDate", "stars"
  ]
}

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