armor-profile

Data Profiling

Analyze table and column statistics, distributions, and data characteristics.

Prerequisites

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

When to Use

• "Profile this table"
• "Show column statistics"
• "What's the data distribution?"
• "Cardinality analysis"
• "Show null rates"
• "Table row counts over time"

Profiling Metrics

Table-Level Metrics

• row_count: Number of rows
• freshness: Time since last update

Column-Level Metrics

• null_rate: Percentage of null values
• distinct_count: Number of unique values (cardinality)
• min/max: Value ranges for numeric/date columns

Steps

1. Get metrics summary for the asset
2. List existing metrics to see what's being tracked
3. View metric snapshots for trends over time
4. Create new metrics if needed for additional coverage

Example Usage

Get Table Profile Summary

from anomalyarmor import Client

client = Client()

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

# List all metrics
metrics = client.metrics.list("asset-uuid")
print("\nMetrics:")
for m in metrics:
    print(f"  {m.metric_type}: {m.name}")
    print(f"    Status: {m.status}")
    if m.last_value is not None:
        print(f"    Last Value: {m.last_value}")

Get Column Statistics

# List column-level metrics
column_metrics = client.metrics.list("asset-uuid", metric_type="null_rate")
print("Null Rates by Column:")
for m in column_metrics:
    print(f"  {m.column_name}: {m.last_value}%")

# Get distinct counts
distinct_metrics = client.metrics.list("asset-uuid", metric_type="distinct_count")
print("\nDistinct Counts:")
for m in distinct_metrics:
    print(f"  {m.column_name}: {m.last_value} unique values")

View Trends Over Time