Datasets

Datasets are collections of structured test data that can be used across your testing activities.

Coming Soon

This feature is currently in development.

Understanding Datasets

What is a Dataset?

A dataset is a structured collection of test data records:

{
  "name": "Test Users",
  "description": "User accounts for e-commerce testing",
  "schema": {
    "id": "integer",
    "email": "string",
    "name": "string",
    "role": "enum:admin,user,guest",
    "created_at": "datetime"
  },
  "records": 1000,
  "tags": ["users", "e-commerce", "regression"]
}

Dataset Types

Type	Description	Use Case
Static	Fixed data records	Regression tests
Dynamic	Generated on demand	Load testing
Snapshot	Point-in-time copy	Production clones
Derived	Transformed from source	Filtered subsets

Creating Datasets

From Template

1. Select a template (e.g., "E-commerce User")
2. Configure generation rules
3. Specify record count
4. Generate dataset

Manual Entry

1. Define schema
2. Add records manually
3. Validate data
4. Save dataset

Import Data

Source	Formats
Files	CSV, JSON, Excel, XML
Database	SQL query results
API	REST/GraphQL responses
Clipboard	Paste from spreadsheet

AI Generation

Let AI create realistic data:

Generate:
  type: User
  count: 1000
  rules:
    - email: unique, realistic
    - name: full name, various nationalities
    - age: between 18 and 65
    - country: weighted by population

Schema Definition

Field Types

Type	Description	Example
string	Text values	"John Doe"
integer	Whole numbers	42
decimal	Floating point	99.99
boolean	True/false	true
datetime	Date and time	"2024-01-15T10:30:00Z"
enum	Fixed options	"active", "inactive"
array	List of values	["tag1", "tag2"]
object	Nested structure	{address: {...}}

Constraints

Schema:
  id:
    type: integer
    required: true
    unique: true
    auto_increment: true

  email:
    type: string
    required: true
    unique: true
    format: email

  age:
    type: integer
    min: 0
    max: 150

  status:
    type: enum
    values: [active, inactive, pending]
    default: pending

Relationships

Define data relationships:

Users:
  id: primary_key

Orders:
  user_id: foreign_key(Users.id)

OrderItems:
  order_id: foreign_key(Orders.id)
  product_id: foreign_key(Products.id)

Data Operations

Querying Data

-- Filter by condition
SELECT * FROM users WHERE status = 'active'

-- Join related data
SELECT orders.*, users.name
FROM orders
JOIN users ON orders.user_id = users.id

-- Aggregate
SELECT COUNT(*) as order_count, user_id
FROM orders
GROUP BY user_id

Transformations

Operation	Description
Filter	Select matching records
Map	Transform field values
Sort	Order by fields
Join	Combine datasets
Aggregate	Group and summarize

Bulk Operations

• Insert multiple records
• Update by condition
• Delete by criteria
• Export subsets

Version Control

Versioning

Every change creates a version:

Version	Date	Author	Changes
v3	Today	You	Added 100 users
v2	Yesterday	Team	Updated emails
v1	Last week	You	Initial creation

Compare Versions

See what changed between versions:

• Added records
• Modified fields
• Deleted records
• Schema changes

Rollback

Restore previous versions:

1. Select target version
2. Preview changes
3. Confirm rollback
4. Create new version from old

Sharing & Access

Permissions

Role	Capabilities
Owner	Full control
Editor	Create, read, update
Viewer	Read only
User	Use in tests

Sharing Options

• Share with team
• Share with project
• Public within organization
• Export for external use

Best Practices

Organization

• Use descriptive names
• Add comprehensive tags
• Document purpose

Data Quality

• Validate schema
• Check constraints
• Remove duplicates

Security

• Mask sensitive data
• Control access
• Audit usage