Quick Start

This guide walks you through your first qualink validation in under 5 minutes.

1. Prepare Your Data

Create a CSV file called users.csv:

user_id,name,email,age
1,Alice,alice@example.com,30
2,Bob,bob@example.com,25
3,Charlie,charlie@example.com,35
4,Diana,diana@example.com,28
5,Eve,eve@example.com,42
6,Frank,frank@example.com,55
7,Grace,,22
8,Hank,hank@example.com,19
9,Ivy,ivy@example.com,31
10,Jack,jack@example.com,45

2. Write Your Validation

import asyncio
from datafusion import SessionContext
from qualink.checks import Check, Level
from qualink.constraints import Assertion
from qualink.core import ValidationSuite
from qualink.formatters import HumanFormatter


async def main() -> None:
    # Create a DataFusion context and register your data
    ctx = SessionContext()
    ctx.register_csv("users", "users.csv")

    # Build and run a validation suite
    result = await (
        ValidationSuite()
        .on_data(ctx, "users")
        .with_name("User Data Quality")
        .add_check(
            Check.builder("Critical Checks")
            .with_level(Level.ERROR)
            .is_complete("user_id")
            .is_unique("email")
            .has_size(Assertion.greater_than(0))
            .build()
        )
        .add_check(
            Check.builder("Data Quality")
            .with_level(Level.WARNING)
            .has_completeness("name", Assertion.greater_than_or_equal(0.95))
            .has_min("age", Assertion.greater_than_or_equal(0))
            .has_max("age", Assertion.less_than_or_equal(120))
            .has_pattern("email", r"@")
            .build()
        )
        .run()
    )

    print(HumanFormatter().format(result))



if __name__ == "__main__":
    asyncio.run(main())

3. Understand the Output

Running the script produces output like:

Verification PASSED: User Data Quality

  Checks: 2  |  Constraints: 7
  Passed: 6  |  Failed: 1  |  Skipped: 0
  Pass rate: 85.7%

  [FAIL] [Critical Checks] Uniqueness of (email) is 0.9000, expected >= 1.0

Issues:
  WARNING Critical Checks / Uniqueness(email): ...

4. Understanding the Flow

  1. SessionContext — DataFusion's entry point for registering data and running SQL
  2. ValidationSuite — The top-level orchestrator
  3. Check — A named group of constraints with a severity Level
  4. Constraint methods — is_complete(), is_unique(), has_size(), etc.
  5. Assertion — Comparison operators like greater_than(), equal_to(), between()
  6. Formatter — Converts the ValidationResult into readable output
📌 Note

All checks are executed via SQL against the DataFusion engine. This means qualink works with any data format DataFusion supports — CSV, Parquet, JSON, and in-memory Arrow tables.

Next Steps