# Synthetic E-Commerce Data Generator (`jungle_synthesizer/synthetic-ecommerce-data-generator`) Actor Generate realistic e-commerce test data with interconnected products, customers, orders, and reviews. Features referential integrity, realistic distributions, temporal coherence, industry presets, and deterministic seed mode. - **URL**: https://apify.com/jungle\_synthesizer/synthetic-ecommerce-data-generator.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Developer tools, E-commerce, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Synthetic E-Commerce Data Generator Generate realistic e-commerce test datasets with four interconnected entity types: products, customers, orders, and reviews. All entities maintain referential integrity — orders reference real product and customer IDs, reviews reference real products and customers. Timestamps maintain temporal coherence: orders are placed after customer registration, shipments follow order placement, deliveries follow shipment. ### Features - Four entity types with cross-references: products, customers, orders, reviews - Referential integrity — every order and review links to real product and customer IDs generated in the same run - Realistic statistical distributions: log-normal product prices, right-skewed review ratings (average ~4.2), weighted order statuses (70% delivered) - Temporal coherence — `shipped_at` always follows `ordered_at`, `delivered_at` follows `shipped_at`, orders are placed after customer registration - Five industry presets with tailored categories, brand names, and price ranges - Deterministic seed mode for reproducible datasets - Five locale options for names, addresses, and phone numbers - No network calls, no proxy needed — pure CPU data generation ### Who Uses Synthetic E-Commerce Data and Why - **E-commerce developers** — populate Shopify, WooCommerce, or Magento staging environments with realistic test data before launch - **Data engineers** — validate ETL pipelines with known-schema e-commerce records that include edge cases (zero-order customers, cancelled orders, one-star reviews) - **Analytics teams** — build and demo dashboards with realistic order volumes, customer segments, and product catalogs without exposing production data - **QA engineers** — stress-test order processing systems with thousands of orders referencing real product inventories and customer accounts - **Bootcamp instructors** — provide students with clean, well-structured datasets for SQL exercises, pandas workshops, and data visualization projects ### How It Works 1. You configure how many products, customers, orders, and reviews to generate, pick an industry preset and locale, and optionally set a random seed. 2. The generator creates products first (with industry-specific categories, brands, and log-normal price distributions), then customers (with segment-weighted lifetime values), then orders (referencing real products and customers, with calculated totals and temporal timestamps), then reviews (with rating-appropriate text templates and referential links). 3. In unified mode, all entities go to one dataset with an `entityType` field. In separate mode, products go to the dataset and other entities are saved as JSON in the key-value store. 4. The `maxItems` cap is applied after generation to limit total output size. ### Input #### Default run — 100 mixed records ```json { "numProducts": 20, "numCustomers": 30, "numOrders": 50, "numReviews": 40, "maxItems": 100, "industry": "general", "locale": "en", "outputFormat": "unified" } ```` #### Electronics dataset with deterministic seed ```json { "numProducts": 50, "numCustomers": 100, "numOrders": 200, "numReviews": 150, "maxItems": 0, "industry": "electronics", "seed": 42, "outputFormat": "unified" } ``` #### Fashion products only (separate mode) ```json { "numProducts": 100, "numCustomers": 50, "numOrders": 80, "numReviews": 60, "maxItems": 0, "industry": "fashion", "outputFormat": "separate" } ``` ### Input Reference | Field | Type | Default | Description | |-------|------|---------|-------------| | `numProducts` | integer | 20 | Number of product records to generate (1–10,000) | | `numCustomers` | integer | 30 | Number of customer records to generate (1–50,000) | | `numOrders` | integer | 50 | Number of order records to generate (0–100,000) | | `numReviews` | integer | 40 | Number of review records to generate (0–100,000) | | `maxItems` | integer | 100 | Maximum total records across all entity types. Set to 0 for no limit | | `industry` | string | general | Industry preset: `general`, `fashion`, `electronics`, `grocery`, `home_goods` | | `locale` | string | en | Locale for names and addresses: `en`, `de`, `fr`, `ja`, `es` | | `seed` | integer | null | Random seed for deterministic output. Omit for random data each run | | `outputFormat` | string | unified | `unified` puts all entities in one dataset. `separate` puts only products in the dataset and saves the rest to the key-value store | ### Output #### Product record ```json { "entityType": "product", "product_id": "PROD-00001", "product_name": "Premium Laptops X7K", "sku": "SKU-RSJ7NHY5", "brand": "NovaTech", "category": "Electronics", "subcategory": "Laptops", "price": 54.56, "cost": 34.63, "weight_kg": 5.68, "rating_avg": 4.2, "review_count": 140, "in_stock": true, "created_at": "2024-03-23T03:33:06.557Z" } ``` #### Customer record ```json { "entityType": "customer", "customer_id": "CUST-00001", "first_name": "Bonita", "last_name": "Tremblay", "email": "bonita.tremblay@hotmail.com", "phone": "(983) 829-9005", "address": "5836 E Main Street", "city": "Flagstaff", "state": "VT", "zip": "75793-8196", "country": "US", "customer_created_at": "2024-03-28T15:21:19.313Z", "lifetime_value": 2450.75, "order_count": 12, "segment": "returning" } ``` #### Order record ```json { "entityType": "order", "order_id": "ORD-00001", "order_customer_id": "CUST-00017", "product_ids": "PROD-00007, PROD-00013, PROD-00002", "quantities": "1, 2, 1", "subtotal": 326.41, "tax": 28.97, "shipping": 0, "total": 355.38, "order_status": "delivered", "ordered_at": "2025-06-22T10:21:51.493Z", "shipped_at": "2025-06-26T10:21:51.493Z", "delivered_at": "2025-06-28T10:21:51.493Z" } ``` #### Review record ```json { "entityType": "review", "review_id": "REV-00001", "review_product_id": "PROD-00003", "review_customer_id": "CUST-00012", "review_rating": 5, "review_title": "Love it!", "review_body": "Absolutely love this Premium Tablets A3M! The build quality is outstanding. Would definitely buy again.", "helpful_count": 7, "verified_purchase": true, "reviewed_at": "2025-08-15T14:30:22.100Z" } ``` ### Industry Presets | Preset | Categories | Price Range | Example Brands | |--------|-----------|-------------|----------------| | `general` | Electronics, Clothing, Home & Kitchen, Sports, Books | $5–$500 | Apex, NovaTech, Zenith | | `fashion` | Women's Clothing, Men's Clothing, Shoes, Accessories, Sportswear | $15–$800 | Luxe & Co, Urban Thread, Maison Noir | | `electronics` | Computers, Mobile, Audio, Smart Home, Gaming | $10–$2,500 | TechVault, PixelForge, Quantum | | `grocery` | Fresh Produce, Dairy & Eggs, Bakery, Beverages, Pantry | $1–$50 | Green Valley, Harvest Moon, Farm Fresh | | `home_goods` | Furniture, Decor, Kitchen, Bedding, Garden | $8–$1,200 | HomeStead, Craftwell, Willow & Oak | ### Performance This actor generates data in-memory with no network calls. Approximate run times: - 100 records: < 1 second - 1,000 records: 1–2 seconds - 10,000 records: 5–10 seconds - 100,000 records: 30–60 seconds Memory usage stays under 256MB for datasets up to 100,000 records. ### Need More Features? If you need additional entity types (inventory, shipping carriers, promotions), custom field mappings, or integration with specific e-commerce platforms, file an issue or get in touch. We are always open to extending the generator to suit your needs. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `numProducts` (type: `integer`): Number of product records to generate ## `numCustomers` (type: `integer`): Number of customer records to generate ## `numOrders` (type: `integer`): Number of order records to generate ## `numReviews` (type: `integer`): Number of review records to generate ## `maxItems` (type: `integer`): Maximum total records across all entity types. Caps the combined output. Set to 0 for no limit. ## `industry` (type: `string`): Choose an industry to get realistic product names, categories, and price ranges ## `locale` (type: `string`): Locale for names, addresses, and phone numbers ## `seed` (type: `integer`): Set a seed for deterministic, reproducible output. Leave empty for random data each run. ## `outputFormat` (type: `string`): How to structure the output dataset. 'unified' puts all entity types in one dataset with an entityType field. 'separate' creates one dataset with products only, and stores customers/orders/reviews in key-value store as JSON files. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "numProducts": 20, "numCustomers": 30, "numOrders": 50, "numReviews": 40, "maxItems": 100, "industry": "general", "locale": "en", "outputFormat": "unified" } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "numProducts": 20, "numCustomers": 30, "numOrders": 50, "numReviews": 40, "maxItems": 100, "industry": "general", "locale": "en", "outputFormat": "unified" }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/synthetic-ecommerce-data-generator").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "numProducts": 20, "numCustomers": 30, "numOrders": 50, "numReviews": 40, "maxItems": 100, "industry": "general", "locale": "en", "outputFormat": "unified", } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/synthetic-ecommerce-data-generator").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "numProducts": 20, "numCustomers": 30, "numOrders": 50, "numReviews": 40, "maxItems": 100, "industry": "general", "locale": "en", "outputFormat": "unified" }' | apify call jungle_synthesizer/synthetic-ecommerce-data-generator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/synthetic-ecommerce-data-generator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Synthetic E-Commerce Data Generator", "description": "Generate realistic e-commerce test data with interconnected products, customers, orders, and reviews. Features referential integrity, realistic distributions, temporal coherence, industry presets, and deterministic seed mode.", "version": "0.1", "x-build-id": "KrXd1NXqRfLhpb53w" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~synthetic-ecommerce-data-generator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-synthetic-ecommerce-data-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~synthetic-ecommerce-data-generator/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-synthetic-ecommerce-data-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~synthetic-ecommerce-data-generator/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-synthetic-ecommerce-data-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "numProducts" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "numProducts": { "title": "Number of Products", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Number of product records to generate", "default": 20 }, "numCustomers": { "title": "Number of Customers", "minimum": 1, "maximum": 50000, "type": "integer", "description": "Number of customer records to generate", "default": 30 }, "numOrders": { "title": "Number of Orders", "minimum": 0, "maximum": 100000, "type": "integer", "description": "Number of order records to generate", "default": 50 }, "numReviews": { "title": "Number of Reviews", "minimum": 0, "maximum": 100000, "type": "integer", "description": "Number of review records to generate", "default": 40 }, "maxItems": { "title": "Max Total Records", "type": "integer", "description": "Maximum total records across all entity types. Caps the combined output. Set to 0 for no limit.", "default": 100 }, "industry": { "title": "Industry Preset", "enum": [ "general", "fashion", "electronics", "grocery", "home_goods" ], "type": "string", "description": "Choose an industry to get realistic product names, categories, and price ranges", "default": "general" }, "locale": { "title": "Locale", "enum": [ "en", "de", "fr", "ja", "es" ], "type": "string", "description": "Locale for names, addresses, and phone numbers", "default": "en" }, "seed": { "title": "Random Seed", "type": "integer", "description": "Set a seed for deterministic, reproducible output. Leave empty for random data each run." }, "outputFormat": { "title": "Output Format", "enum": [ "unified", "separate" ], "type": "string", "description": "How to structure the output dataset. 'unified' puts all entity types in one dataset with an entityType field. 'separate' creates one dataset with products only, and stores customers/orders/reviews in key-value store as JSON files.", "default": "unified" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```