armor-quality

Data Quality

Set up and manage data quality checks including metrics (row counts, null rates, distinct counts) and validity rules (null checks, uniqueness, custom expressions).

Prerequisites

• AnomalyArmor API key configured (~/.armor/config.yaml or ARMOR_API_KEY env var)
• Python SDK installed (pip install anomalyarmor)

When to Use

• "Add a null check to the email column"
• "Create a row count metric for orders"
• "What metrics exist for this table?"
• "Add a uniqueness check on customer_id"
• "Show data quality status"
• "Set up a freshness metric"

Concepts

Metrics

Metrics track quantitative measurements over time:

• row_count: Number of rows in a table
• null_rate: Percentage of null values in a column
• distinct_count: Number of unique values
• freshness: Time since last update

Validity Rules

Rules that validate data integrity:

• NOT_NULL: Column must not contain nulls
• UNIQUE: Column values must be unique
• ACCEPTED_VALUES: Column values must be in allowed list
• REGEX: Column values must match pattern
• CUSTOM: Custom SQL expression

Steps

Creating a Metric

1. Get the asset ID for the target table
2. Choose metric type (row_count, null_rate, distinct_count, etc.)
3. Call client.metrics.create() with appropriate parameters
4. Optionally trigger immediate capture with client.metrics.capture()

Creating a Validity Rule

1. Get the asset ID for the target table
2. Choose rule type (NOT_NULL, UNIQUE, ACCEPTED_VALUES, etc.)
3. Call client.validity.create() with column and rule parameters
4. Optionally run immediate check with client.validity.check()

Example Usage

List Existing Metrics

from anomalyarmor import Client

client = Client()

# Get metrics summary
summary = client.metrics.summary("asset-uuid")
print(f"Total metrics: {summary.total_metrics}")
print(f"Failing metrics: {summary.failing_count}")

# List all metrics
metrics = client.metrics.list("asset-uuid")
for m in metrics:
    print(f"  {m.metric_type}: {m.name} ({m.status})")