Set Up Monitoring

Name: armor-monitor
Rating: 78
Author: anomalyarmor

Configure freshness monitoring and schema drift detection for your data assets.

Prerequisites

•AnomalyArmor API key configured (~/.armor/config.yaml or ARMOR_API_KEY env var)
•Python SDK installed (pip install anomalyarmor)
•Data source already connected (use /armor:connect first)

When to Use

•"Set up freshness monitoring for the orders table"
•"Monitor my critical tables"
•"Enable schema drift detection"
•"Alert me when data is stale"
•"Track schema changes"

Steps

For Freshness Monitoring

•Identify the asset and table to monitor
•Determine check interval (how often to check)
•Choose monitoring mode (auto_learn or explicit threshold)
•Create schedule with client.freshness.create_schedule()

For Schema Monitoring

•Identify the asset to monitor
•Create baseline with client.schema.create_baseline()
•Enable monitoring with client.schema.enable_monitoring()

Example Usage

Set Up Freshness Monitoring (Auto-Learn)

python

from anomalyarmor import Client

client = Client()

# List existing schedules for the asset
schedules = client.freshness.list_schedules(asset_id="asset-uuid")
print(f"Existing schedules: {len(schedules)}")

# Create freshness schedule with auto-learn
# System will learn normal update patterns and alert on deviations
schedule = client.freshness.create_schedule(
    asset_id="asset-uuid",
    table_path="public.orders",
    check_interval="1h",  # Check every hour
    monitoring_mode="auto_learn"
)

print(f"Created schedule: {schedule.id}")
print(f"Table: {schedule.table_path}")
print(f"Check interval: {schedule.check_interval}")

Set Up Freshness with Explicit Threshold

python

from anomalyarmor import Client

client = Client()

# Create schedule with explicit threshold
# Alert if table hasn't updated in 24 hours
schedule = client.freshness.create_schedule(
    asset_id="asset-uuid",
    table_path="public.daily_summary",
    check_interval="6h",
    monitoring_mode="explicit",
    expected_interval_hours=24,
    freshness_column="updated_at"  # Optional: specify column
)

print(f"Created schedule with {schedule.expected_interval_hours}h threshold")

Enable Schema Drift Monitoring

python

from anomalyarmor import Client

client = Client()

# Create schema baseline (captures current schema)
baseline = client.schema.create_baseline(
    asset_id="asset-uuid",
    description="Initial production baseline"
)
print(f"Baseline captured: {baseline.column_count} columns")

# Enable monitoring with daily checks
config = client.schema.enable_monitoring(
    asset_id="asset-uuid",
    schedule_type="daily",  # hourly, every_4_hours, daily, weekly
    auto_create_baseline=True  # Create baseline if none exists
)
print(f"Monitoring enabled, next check: {config.next_check_at}")

Disable Monitoring

python

from anomalyarmor import Client

client = Client()

# Delete freshness schedule
client.freshness.delete_schedule("schedule-uuid")

# Disable schema monitoring (keeps baseline)
client.schema.disable_monitoring("asset-uuid")

Check Interval Options

Interval	Description	Best For
`5m`	Every 5 minutes	Real-time data
`1h`	Hourly	Frequently updated tables
`6h`	Every 6 hours	Moderate update frequency
`1d`	Daily	Daily batch jobs
`1w`	Weekly	Weekly reports

Schedule Type Options (Schema)

Type	Description
`hourly`	Check every hour
`every_4_hours`	Check every 4 hours
`daily`	Check once per day
`weekly`	Check once per week

Monitoring Mode

•auto_learn (recommended): System learns normal update patterns and alerts on deviations
•explicit: You specify expected update interval; alerts if exceeded

Follow-up Actions

•Use /armor:status to verify monitoring is working
•Use /armor:alerts to see triggered alerts
•Adjust thresholds based on false positives/negatives