Validation Monitor

object · required

Supported modes: fixed, dynamic, manual. Crontab (interval_crontab) is supported.

Properties

type — schedule type

enum · optional · default: fixed

Accepted values: fixed · dynamic · manual

schedule:
  type: fixed

interval_minutes — run interval for fixed schedules

integer · optional

Required when type is fixed.

schedule:
  type: fixed
  interval_minutes: 720

interval_crontab — cron expressions

array of strings · optional

5-field cron format.

schedule:
  type: fixed
  interval_crontab:
    - "0 8 * * *"

interval_crontab_day_operator — day-of-week/day-of-month combination

enum · optional

Accepted values: AND · OR

schedule:
  interval_crontab_day_operator: AND

start_time — ISO 8601 start time

string · optional

schedule:
  start_time: "2024-01-01T08:00:00Z"

timezone — IANA timezone

string · optional

schedule:
  timezone: America/Los_Angeles

dynamic_schedule_tables — tables that trigger the monitor

array of strings · optional (required when type is dynamic)

schedule:
  type: dynamic
  dynamic_schedule_tables:
    - analytics:public.orders

dynamic_schedule_jobs — jobs that trigger the monitor

array of objects · optional

schedule:
  type: dynamic
  dynamic_schedule_jobs:
    - job_type: AirflowDag
      job_name: etl_orders
      project_name: my-airflow

min_interval_minutes — minimum interval for dynamic schedules

integer · optional

schedule:
  type: dynamic
  min_interval_minutes: 30

schedule:
  type: fixed
  interval_minutes: 720

object · required

Provide either table or sql, not both.

Properties

table — fully qualified table name

string · required (if sql is not provided)

Format: database:schema.table.

data_source:
  table: "analytics_db:public.orders"

sql — SQL query returning rows to validate

string · required (if table is not provided)

data_source:
  sql: |
    SELECT * FROM orders
    WHERE created_at >= CURRENT_DATE - 1

transforms — AI-powered field transforms

array of objects · optional

See data_source.transforms in the Metric Monitor reference.

data_source:
  table: "analytics_db:public.orders"
  transforms:
    - field: status
      transform: normalize

data_source:
  table: "analytics_db:public.orders"

object · required

A predicate tree. The root node must be a GROUP. Each node is one of four types: GROUP, UNARY, BINARY, or SQL. Conditions match invalid data — a row that matches is a breach. Validation monitors do not use operators or threshold types — alerting is determined by the predicate tree and optional percentage_threshold/percentage_operator fields.

Noise reduction: Set event_rollup_count (min 2) or event_rollup_until_changed at the monitor level to suppress notifications until N consecutive breaches or until the result changes. These two fields are mutually exclusive.

🚧

alert_condition is singular.

Other monitor types use alert_conditions (plural, an array). Validation monitors use alert_condition (singular, a single object). The plural form causes a validation error.

Properties

GROUP node — combine conditions with AND/OR logic

The root alert_condition must be a GROUP. Groups can be nested for complex logic.

alert_condition:
  type: GROUP
  operator: AND
  conditions:
    - type: UNARY
      predicate:
        name: "null"
      value:
        - field: user_id

UNARY predicate — single-field check with no comparison value

Available UNARY predicates by field type:

📘

Predicate availability varies by warehouse

Run get_validation_predicates to get the current list for your warehouse.

- type: UNARY
  predicate:
    name: "null"
  value:
    - field: email

BINARY predicate — field-to-value comparison

Available BINARY predicates by field type:

Right-side value types:

🚧

Left side must always be a field reference.

You cannot put a literal, sql, or aggregate on the left side.

- type: BINARY
  predicate:
    name: greater_than
  left:
    - field: age
  right:
    - literal: "150"

SQL predicate — free-form SQL boolean expression

- type: SQL
  sql: "status = 'failed' AND amount < 0"

Negation — invert any UNARY or BINARY predicate

Any UNARY or BINARY predicate can be inverted with negated: true in the predicate object.

# Alert when status is NOT in the allowed set
- type: BINARY
  predicate:
    name: in_set
    negated: true
  left:
    - field: status
  right:
    - literal: "active"
    - literal: "inactive"
    - literal: "pending"

alert_condition:
  type: GROUP
  operator: AND
  conditions:
    - type: UNARY
      predicate:
        name: "null"
      value:
        - field: user_id

string · required

Displayed in the Monte Carlo UI and in incident notifications. Max 512 characters.

description: "Alert when user_id is null in orders table"

array of strings (exactly one entry) · required on all accounts created after January 2025

Set default_domain in montecarlo.yml to avoid repeating it on every monitor.

domains:
  - my-domain

number · optional

Alert only when the percentage of invalid rows exceeds this value. Minimum: 0. When omitted, the monitor alerts on any matching row. Must be paired with percentage_operator — setting one without the other causes a validation error.

percentage_threshold: 5
percentage_operator: GT

enum · optional

Accepted values: EQ · GT · GTE · LT · LTE

Must be paired with percentage_threshold — setting one without the other causes a validation error.

percentage_threshold: 1.0
percentage_operator: GT

string · optional (yes if multiple warehouses)

Warehouse UUID or name. Overrides default_resource from montecarlo.yml.

warehouse: my-snowflake

string · required

Required for monitors created after Jan 29, 2024 (existing monitors keep working). Changing the name creates a new monitor and deletes the old one — incident history does not transfer.

name: null_user_id_check

string · optional

Overrides the default connection for this warehouse.

connection_name: analytics-readonly

integer · optional

Minimum 2. Mutually exclusive with event_rollup_until_changed.

event_rollup_count: 3

boolean · optional · default: false

Notifies only on status transitions. Mutually exclusive with event_rollup_count.

event_rollup_until_changed: true

string · optional

Track specific invalid rows across runs — see whether the same rows keep failing or new ones appear.

exception_primary_key_column: order_id

enum · optional

Accepted values: SEV-0 · SEV-1 · SEV-2 · SEV-3 · SEV-4

severity: SEV-2

enum · optional

Accepted values: P1 · P2 · P3 · P4 · P5

priority: P2

array of strings · optional · default: []

Audience names linking this monitor to channels defined in Notifications as Code. In exported/rendered YAML, appears as labels.

audiences:
  - data-eng-oncall
  - platform-alerts

array of strings · optional

Separate audience for run failures. Defaults to audiences when omitted.

failure_audiences:
  - data-eng-oncall

boolean · optional

notify_run_failure: true

integer · optional

timeout: 300

array of objects · optional · default: []

tags:
  - name: team
    value: analytics
  - name: environment
    value: production

enum · optional

Accepted values: ACCURACY · COMPLETENESS · CONSISTENCY · TIMELINESS · UNIQUENESS · VALIDITY

data_quality_dimension: VALIDITY

string · optional

Visible in the Monte Carlo UI. Not included in notifications.

notes: Owned by the analytics team. Reviewed quarterly.

boolean · optional · default: false

Creates the monitor in a paused state. Omitting this on a later update resets to false (active) due to PUT semantics — always include it if you want the monitor to stay in draft.

is_draft: true

string · optional

Include the UUID of an existing monitor to update it instead of creating a new one.

uuid: 0dae7702-0950-45c7-909c-7e183bddca19