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Documentation Index

Fetch the complete documentation index at: https://docs.anomalyarmor.ai/llms.txt

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As an analytics engineer, you own the quality of data that powers business decisions. AnomalyArmor helps you catch data quality issues before they reach dashboards.
Analytics Engineer Journey: Connect (~5 min) → Configure (~5 min) → Monitor (~5 min) → Report (~5 min)

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Your Key Workflows

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Set Freshness SLAs

Stakeholders expect data to be current. Set SLAs to get alerted when data is stale:
1

Identify Critical Tables

Start with tables that power executive dashboards or customer-facing reports.
2

Set Freshness Expectations

Define how often data should be updated (hourly, daily, weekly). Configure freshness
3

Route Alerts to Slack

Get notified in your team channel when SLAs are violated. Set up Slack

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Track Data Quality Metrics

Go beyond freshness. Monitor row counts, null percentages, and more: 
Critical Table Monitoring:
+-- row_count: Catch unexpected data loss or growth
+-- null_percent on key columns: Detect incomplete data loads
+-- distinct_count on IDs: Identify duplicate records
Create your first metric

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Report Quality to Stakeholders

Embed data quality status directly in dashboards: 
# Sales Dashboard
![Data Quality](https://app.anomalyarmor.ai/badges/abc123.svg)
When the badge is green, stakeholders know the data is reliable. Set up report badges

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Document Tables with AI

Use Intelligence to auto-generate descriptions for your dbt models: 
from anomalyarmor import Client

client = Client()

# Generate descriptions for all columns
descriptions = client.intelligence.describe(
    "snowflake.prod.warehouse.orders"
)
# Paste into your dbt YAML files
Use Intelligence
FeatureWhy You Need It
Freshness MonitoringSet SLAs on data recency
Data Quality MetricsTrack statistical properties over time
Report BadgesShow quality status in dashboards
Slack AlertsGet notified in your team channel

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Common Tasks

Set Up Freshness SLAs

Define expected update frequency

Create Metrics

Track row counts, null percentages, and more

Embed Report Badges

Show quality status in Looker, Tableau, Notion

Object Profiles

AI-generated intelligence profiles for every table

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Common Questions

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How do I set freshness SLAs on my dbt models?

Identify the tables that feed executive or customer-facing dashboards, then set freshness thresholds per table (hourly, daily, weekly). Route alerts to your team’s Slack channel. See Freshness Monitoring.

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Can I show data quality status inside my Looker or Tableau dashboards?

Yes. Embed a report badge that turns green or red based on freshness and quality monitors. Stakeholders can see at a glance whether the underlying data is trustworthy. See Report Badges.

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How do I generate column descriptions for my dbt YAML files?

Use the Intelligence API (client.intelligence.describe(...)) to auto-generate descriptions and paste them into your schema.yml. See Object Profiles for the full workflow.

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What metrics should I track on my most important tables?

Start with row count, null percentage on required columns, and distinct count on ID columns. Those three catch the majority of real-world quality issues (data loss, incomplete loads, duplicate inserts). See Data Quality Metrics.