Context example data (eCommerce)

Serverless Stack

Complete the following installation steps to index the sample eCommerce orders data into Elasticsearch. You can run any of the context examples against this sample data after you've configured it.

Each document in this dataset represents a complete eCommerce order. Every order contains complete transaction data including product details, pricing information, customer data, and geographic location. Orders might include multiple products, with product-specific information stored as values within individual fields.

1. Go to Integrations and search for Sample Data.
2. On the Sample data page, expand the Other sample data sets. <image1>
3. Click Add data to install Sample eCommerce orders. <image2> <image3>
4. Verify Installation: Navigate to Analytics > Discover and select the kibana_sample_data_ecommerce data view. You should see eCommerce order documents with complete customer, product, and transaction information.

Here’s an example of a complete eCommerce order document with two products (basic T-shirt and boots):

{
  "order_id": 578286,
  "order_date": "2025-08-13T16:53:46+00:00",
  "type": "order",
  "currency": "EUR",
  "customer_id": 39,
  "customer_full_name": "Kamal Brock",
  "customer_first_name": "Kamal",
  "customer_last_name": "Brock",
  "customer_gender": "MALE",
  "customer_phone": "",
  "email": "kamal@brock-family.zzz",
  "user": "kamal",
  "day_of_week": "Wednesday",
  "day_of_week_i": 2,
  "geoip": {
    "city_name": "Istanbul",
    "region_name": "Istanbul",
    "country_iso_code": "TR",
    "continent_name": "Asia",
    "location": {
  "lat": 41,
  "lon": 29
    }
  },
  "category": [
    "Men's Clothing",
    "Men's Shoes"
  ],
  "manufacturer": [
    "Elitelligence",
    "Oceanavigations"
  ],
  "sku": [
    "ZO0548305483",
    "ZO0256702567"
  ],
  "products": [
    {
  "_id": "sold_product_578286_15939",
  "product_id": 15939,
  "product_name": "Basic T-shirt - khaki",
  "category": "Men's Clothing",
  "manufacturer": "Elitelligence",
  "sku": "ZO0548305483",
  "base_price": 7.99,
  "base_unit_price": 7.99,
  "price": 7.99,
  "taxful_price": 7.99,
  "taxless_price": 7.99,
  "quantity": 1,
  "discount_amount": 0,
  "discount_percentage": 0,
  "tax_amount": 0,
  "created_on": "2016-12-21T16:53:46+00:00"
    },
    {
  "_id": "sold_product_578286_1844",
  "product_id": 1844,
  "product_name": "Boots - beige",
  "category": "Men's Shoes",
  "manufacturer": "Oceanavigations",
  "sku": "ZO0256702567",
  "base_price": 84.99,
  "base_unit_price": 84.99,
  "price": 84.99,
  "taxful_price": 84.99,
  "taxless_price": 84.99,
  "quantity": 1,
  "discount_amount": 0,
  "discount_percentage": 0,
  "tax_amount": 0,
  "created_on": "2016-12-21T16:53:46+00:00"
    }
  ],
  "taxful_total_price": 92.98,
  "taxless_total_price": 92.98,
  "total_quantity": 2,
  "total_unique_products": 2,
  "event": {
    "dataset": "sample_ecommerce"
  }
}
		

• Order information

  order_id (keyword): Unique identifier for each order.

  order_date (date): Timestamp when the order was placed.

  type (keyword): Document type (always "order").

  currency (keyword): Transaction currency (EUR, USD, etc.).

• Customer information

  customer_id (keyword): Unique customer identifier.

  customer_full_name (text): Complete customer name with keyword subfield.

  customer_first_name (text): Customer's first name with keyword subfield.

  customer_last_name (text): Customer's last name with keyword subfield.

  customer_gender (keyword): Customer gender (MALE, FEMALE).

  email (keyword): Customer email address.

  user (keyword): Username derived from customer name.

• Geographic Information

  geoip.city_name (keyword): City where the order was placed.

  geoip.continent_name (keyword): Continent of order origin.

  geoip.country_iso_code (keyword): Two-letter country code.

  geoip.location (geo_point): Geographic coordinates of order location.

  geoip.region_name (keyword): State or region name.

• Order timing

  day_of_week (keyword): Day when the order was placed (Monday, Tuesday, and so on).

  day_of_week_i (integer): Numeric day of week (0-6).

• Product information

  category (text): Primary product categories with keyword subfield.

  manufacturer (text): Manufacturer names with keyword subfield.

  sku (keyword): Stock Keeping Unit codes.

  products.product_name (text): Product names (comma-separated for multiple products).

  products.product_id (long): Product identifiers.

  products._id (text): Internal product identifiers with keyword subfield.

  products.sku (keyword): Product-specific SKU codes.

  products.category (text): Individual product categories with keyword subfield.

  products.manufacturer (text): Product-specific manufacturers with keyword subfield.

  products.created_on (date): Product catalog creation dates.

  products.quantity (integer): Quantity of each product ordered.

• Pricing and financial information

  products.base_price (half_float): Original product prices before discounts.

  products.base_unit_price (half_float): Base price per unit.

  products.price (half_float): Final product prices.

  products.min_price (half_float): Minimum price thresholds.

  products.discount_amount (half_float): Discount amounts applied.

  products.discount_percentage (half_float) : Percentage discounts applied.

  products.unit_discount_amount (half_float): Discount amount per unit.

  products.tax_amount (half_float): Tax amounts for products.

  products.taxful_price (half_float): Product prices including tax.

  products.taxless_price (half_float): Product prices excluding tax.

  taxful_total_price (half_float): Total order amount including tax.

  taxless_total_price (half_float): Total order amount excluding tax.

• Order summary

  total_quantity (integer): Total items in the order.

  total_unique_products (integer): Number of different products in the order.

• Metadata

  event.dataset (keyword): Dataset identifier ("sample_ecommerce").