E-Commerce Transactions → CSV

Generate a shaped CSV dataset of purchases, refunds, and chargebacks for analytics, database seeding, or ML training.

Build a generator that produces an e-commerce transaction dataset as a CSV file. Transactions include purchases, refunds, and chargebacks with realistic distributions — 80% purchases, 15% refunds, 5% chargebacks. The result is a ready-to-import CSV with correlated fields (customer, product, amount).

What you'll build

The generator uses:

linspace input — 10,000 timestamps spread evenly across a date range.
chance picking mode — weighted random selection between three transaction types.
file output with a template formatter — each event becomes a CSV row.
JSON samples — product catalog loaded from a file.
shared state — running revenue counter across all events.

The entire dataset is generated in one burst using sample mode (live_mode: false), so 10,000 rows complete in seconds.

Prerequisites

Eventum installed

Project structure

generator.yml

purchase.jinja

refund.jinja

chargeback.jinja

products.json

Build it

Create the project directory

mkdir -p ecommerce-csv/{templates,data}
cd ecommerce-csv

Create the product catalog

A JSON sample file with product names, categories, and price ranges. Each transaction picks a random product from this list.

data/products.json

[
  { "name": "Wireless Mouse", "category": "Electronics", "min_price": 15, "max_price": 45 },
  { "name": "USB-C Hub", "category": "Electronics", "min_price": 25, "max_price": 80 },
  { "name": "Desk Lamp", "category": "Home", "min_price": 20, "max_price": 60 },
  { "name": "Notebook Set", "category": "Office", "min_price": 8, "max_price": 25 },
  { "name": "Bluetooth Speaker", "category": "Electronics", "min_price": 30, "max_price": 120 },
  { "name": "Coffee Mug", "category": "Home", "min_price": 10, "max_price": 30 },
  { "name": "Backpack", "category": "Accessories", "min_price": 35, "max_price": 90 },
  { "name": "Webcam", "category": "Electronics", "min_price": 40, "max_price": 150 },
  { "name": "Plant Pot", "category": "Home", "min_price": 12, "max_price": 35 },
  { "name": "Mechanical Keyboard", "category": "Electronics", "min_price": 60, "max_price": 200 }
]

Write transaction templates

Each template produces a single CSV row. All three templates share the same column order: transaction_id, timestamp, type, customer_id, customer_name, product, category, amount, currency.

Purchase — the most common transaction. Picks a random product and generates a price within its range.

templates/purchase.jinja

{% set product = module.rand.choice(samples.products) %}
{% set amount = module.rand.number.floating(product.min_price, product.max_price) %}
{% do shared.set("revenue", shared.get("revenue", 0) + amount) %}
{{ module.rand.crypto.uuid4() }},{{ timestamp.strftime("%Y-%m-%d %H:%M:%S") }},purchase,C-{{ module.rand.number.integer(10000, 99999) }},{{ module.faker.locale.en.name() }},{{ product.name }},{{ product.category }},{{ "%.2f" | format(amount) }},USD

JSON sample rows support named access via object keys: product.name, product.category, product.min_price, product.max_price.

Refund — returns a product. The amount is negative.

templates/refund.jinja

{% set product = module.rand.choice(samples.products) %}
{% set amount = module.rand.number.floating(product.min_price, product.max_price) %}
{% do shared.set("revenue", shared.get("revenue", 0) - amount) %}
{{ module.rand.crypto.uuid4() }},{{ timestamp.strftime("%Y-%m-%d %H:%M:%S") }},refund,C-{{ module.rand.number.integer(10000, 99999) }},{{ module.faker.locale.en.name() }},{{ product.name }},{{ product.category }},-{{ "%.2f" | format(amount) }},USD

Chargeback — disputed transaction. Rare (5% of total).

templates/chargeback.jinja

{% set product = module.rand.choice(samples.products) %}
{% set amount = module.rand.number.floating(product.min_price, product.max_price) %}
{% do shared.set("revenue", shared.get("revenue", 0) - amount) %}
{{ module.rand.crypto.uuid4() }},{{ timestamp.strftime("%Y-%m-%d %H:%M:%S") }},chargeback,C-{{ module.rand.number.integer(10000, 99999) }},{{ module.faker.locale.en.name() }},{{ product.name }},{{ product.category }},-{{ "%.2f" | format(amount) }},USD

The shared.set("revenue", ...) call tracks a running total across all events. This doesn't appear in the CSV — it's an example of cross-template coordination. You could use it in a summary template or inspect it via the Studio State tab.

Configure the generator

The chance picking mode assigns a probability to each template. The chances must sum to 1.0.

generator.yml

input:
  - linspace:
      start: "2025-01-01T00:00:00"
      end: "2025-12-31T23:59:59"
      count: 10000
      endpoint: true

event:
  template:
    mode: chance
    samples:
      products:
        type: json
        source: data/products.json
    templates:
      - purchase:
          template: templates/purchase.jinja
          chance: 0.80
      - refund:
          template: templates/refund.jinja
          chance: 0.15
      - chargeback:
          template: templates/chargeback.jinja
          chance: 0.05

output:
  - file:
      path: transactions.csv
      write_mode: overwrite

Key decisions:

linspace spreads 10,000 timestamps evenly across the entire year 2025 — about one transaction every 53 minutes.
chance mode: 80% of timestamps render the purchase template, 15% refund, 5% chargeback.
The file output uses write_mode: overwrite so each run starts fresh. The default plain formatter passes each CSV row through as-is.

Run it

Use eventum generate in sample mode for fast, one-shot generation:

eventum generate --path generator.yml --id ecommerce --live-mode false

The --live-mode false flag releases all 10,000 timestamps instantly instead of waiting for their scheduled wall-clock times. The entire dataset generates in a few seconds.

Add a CSV header and check the output:

sed -i '1i transaction_id,timestamp,type,customer_id,customer_name,product,category,amount,currency' transactions.csv
head -5 transactions.csv

transaction_id,timestamp,type,customer_id,customer_name,product,category,amount,currency
f47ac10b-58cc-4372-a567-0e02b2c3d479,2025-01-01 00:00:00,purchase,C-38472,John Smith,Wireless Mouse,Electronics,32.15,USD
8a3b5c7d-1234-4e6f-9abc-def012345678,2025-01-01 00:53:00,purchase,C-91034,Maria Garcia,Webcam,Electronics,89.50,USD
c9d1e2f3-4567-4890-abcd-ef1234567890,2025-01-01 01:46:00,refund,C-55219,Alex Johnson,Desk Lamp,Home,-42.30,USD
a1b2c3d4-5678-4901-bcde-f12345678901,2025-01-01 02:39:00,purchase,C-72841,Sarah Williams,Backpack,Accessories,67.80,USD

You should see approximately 8,000 purchases, 1,500 refunds, and 500 chargebacks. Check the distribution:

tail -n +2 transactions.csv | cut -d',' -f3 | sort | uniq -c | sort -rn

Going further

Add customer loyalty — use shared state to track repeat customers by keeping a customer pool and reusing IDs with some probability.
Seasonal patterns — replace linspace with time-patterns using a triangular distribution to create Black Friday and holiday spikes.
Multiple currencies — add params for currency and run multiple generators in parallel with different currency settings.
Database seeding — swap the file output for ClickHouse to populate a transactions table directly.

E-Commerce Transactions → CSV

What you'll build

Prerequisites

Project structure

Build it

Create the project directory

Create the product catalog

Write transaction templates

Configure the generator

Run it

Going further

What's next

linspace reference

Formatters

File output

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