Add docs for data_freshness_sla and volume_threshold tests

docs/data-tests/data-freshness-sla.mdx

@@ -0,0 +1,143 @@
+---
+title: "data_freshness_sla"
+sidebarTitle: "Data Freshness SLA"
+---
+
+import AiGenerateTest from '/snippets/ai-generate-test.mdx';
+
+
+<AiGenerateTest />
+
+`elementary.data_freshness_sla`
+
+Verifies that data in a model was updated before a specified SLA deadline time.
+
+This test checks the maximum timestamp value of a specified column in your data to determine whether the data was refreshed before your deadline. Unlike `freshness_anomalies` (which uses ML-based anomaly detection), this test validates against a fixed, explicit SLA time — making it ideal when you have a concrete contractual or operational deadline.
+
+### Use Case
+
+"Was the data in my model updated before 7 AM Pacific today?"
+
+### Test Logic
+
+1. If today is not a scheduled check day → **PASS** (skip)
+2. Query the model for the maximum value of `timestamp_column`
+3. If the max timestamp is from today → **PASS** (data is fresh)
+4. If the SLA deadline hasn't passed yet → **PASS** (still time)
+5. If the max timestamp is from a previous day → **FAIL** (DATA_STALE)
+6. If no data exists in the table → **FAIL** (NO_DATA)
+
+### Test configuration
+
+_Required configuration: `timestamp_column`, `sla_time`, `timezone`_
+
+{/* prettier-ignore */}
+<pre>
+ <code>
+  data_tests:
+    &nbsp;&nbsp;-- elementary.data_freshness_sla:
+      &nbsp;&nbsp;&nbsp;&nbsp;arguments:
+        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<font color="#CD7D55">timestamp_column: column name</font> # Required - timestamp column to check for freshness
+        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<font color="#CD7D55">sla_time: string</font> # Required - e.g., "07:00", "7am", "2:30pm", "14:30"
+        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<font color="#CD7D55">timezone: string</font> # Required - IANA timezone name, e.g., "America/Los_Angeles"
+        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<font color="#CD7D55">day_of_week: string | array</font> # Optional - Day(s) to check: "Monday" or ["Monday", "Wednesday"]
+        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<font color="#CD7D55">day_of_month: int | array</font> # Optional - Day(s) of month to check: 1 or [1, 15]
+        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="/data-tests/anomaly-detection-configuration/where-expression"><font color="#CD7D55">where_expression: sql expression</font></a> # Optional - filter the data before checking
+ </code>
+</pre>
+
+<RequestExample>
+
+```yml Models
+models:
+  - name: < model name >
+    data_tests:
+      - elementary.data_freshness_sla:
+          arguments:
+            timestamp_column: < column name > # Required
+            sla_time: < deadline time > # Required - e.g., "07:00", "7am", "2:30pm"
+            timezone: < IANA timezone > # Required - e.g., "America/Los_Angeles"
+            day_of_week: < day or array > # Optional
+            day_of_month: < day or array > # Optional
+            where_expression: < sql expression > # Optional
+```
+
+```yml Daily check
+models:
+  - name: daily_revenue
+    data_tests:
+      - elementary.data_freshness_sla:
+          arguments:
+            timestamp_column: updated_at
+            sla_time: "07:00"
+            timezone: "America/Los_Angeles"
+          config:
+            tags: ["elementary"]
+            severity: error
+```
+
+```yml With filter expression
+models:
+  - name: daily_events
+    data_tests:
+      - elementary.data_freshness_sla:
+          arguments:
+            timestamp_column: event_timestamp
+            sla_time: "6am"
+            timezone: "Europe/Amsterdam"
+            where_expression: "event_type = 'completed'"
+          config:
+            tags: ["elementary"]
+```
+
+```yml Weekly - only Mondays
+models:
+  - name: weekly_report_data
+    data_tests:
+      - elementary.data_freshness_sla:
+          arguments:
+            timestamp_column: report_date
+            sla_time: "09:00"
+            timezone: "Asia/Tokyo"
+            day_of_week: ["Monday"]
+          config:
+            tags: ["elementary"]
+```
+
+</RequestExample>
+
+### Features
+
+- **Data-level freshness**: Checks actual data timestamps, not just pipeline execution time
+- **Flexible time formats**: Supports `"07:00"`, `"7am"`, `"2:30pm"`, `"14:30"`, and other common formats
+- **IANA timezone support**: Uses standard timezone names like `"America/Los_Angeles"`, `"Europe/Amsterdam"`, etc.
+- **Automatic DST handling**: Uses `pytz` for timezone conversions with automatic daylight saving time handling
+- **Database-agnostic**: All timezone logic happens at compile time
+- **Schedule filters**: Optional `day_of_week` and `day_of_month` parameters to check only specific days
+- **Filter support**: Use `where_expression` to check freshness of a specific subset of data
+
+### Parameters
+
+| Parameter          | Required | Description                                                    |
+| ------------------ | -------- | -------------------------------------------------------------- |
+| `timestamp_column` | Yes      | Column name containing timestamps to check for freshness       |
+| `sla_time`         | Yes      | Deadline time (e.g., `"07:00"`, `"7am"`, `"2:30pm"`)          |
+| `timezone`         | Yes      | IANA timezone name (e.g., `"America/Los_Angeles"`)             |
+| `day_of_week`      | No       | Day(s) to check: `"Monday"` or `["Monday", "Wednesday"]`      |
+| `day_of_month`     | No       | Day(s) of month to check: `1` or `[1, 15]`                    |
+| `where_expression` | No       | SQL expression to filter the data before checking              |
+
+### Comparison with other freshness tests
+
+| Feature | `data_freshness_sla` | `freshness_anomalies` | `execution_sla` |
+| --- | --- | --- | --- |
+| What it checks | Data timestamps | Data timestamps | Pipeline run time |
+| Detection method | Fixed SLA deadline | ML-based anomaly detection | Fixed SLA deadline |
+| Best for | Contractual/operational deadlines | Detecting unexpected delays | Pipeline execution deadlines |
+| Works with sources | Yes | Yes | No (models only) |
+
+### Notes
+
+- The `timestamp_column` values are assumed to be in **UTC** (or timezone-naive timestamps that represent UTC). If your data stores local timestamps, the comparison may be incorrect.
+- If both `day_of_week` and `day_of_month` are set, the test uses OR logic (checks if either matches)
+- The test passes if the SLA deadline hasn't been reached yet, giving your data time to be updated

docs/data-tests/volume-threshold.mdx

@@ -0,0 +1,152 @@
+---
+title: "volume_threshold"
+sidebarTitle: "Volume Threshold"
+---
+
+import AiGenerateTest from '/snippets/ai-generate-test.mdx';
+
+
+<AiGenerateTest />
+
+`elementary.volume_threshold`
+
+Monitors row count changes between time buckets using configurable percentage thresholds with multiple severity levels.
+
+Unlike `volume_anomalies` (which uses ML-based anomaly detection to determine what's "normal"), this test lets you define explicit percentage thresholds for warnings and errors — giving you precise control over when to be alerted. It uses Elementary's metric caching infrastructure to avoid recalculating row counts for buckets that have already been computed.
+
+### Use Case
+
+"Alert me if my table's row count drops or spikes by more than 10% compared to the previous period."
+
+### Test Logic
+
+1. Collect row count metrics per time bucket (using Elementary's incremental metric caching)
+2. Compare the most recent completed bucket against the previous bucket
+3. Calculate the percentage change between the two
+4. If the previous bucket has fewer rows than `min_row_count` → **PASS** (insufficient baseline)
+5. If the absolute change exceeds `error_threshold_percent` → **ERROR**
+6. If the absolute change exceeds `warn_threshold_percent` → **WARN**
+7. Otherwise → **PASS**
+
+### Test configuration
+
+_Required configuration: `timestamp_column`_
+
+{/* prettier-ignore */}
+<pre>
+ <code>
+  data_tests:
+    &nbsp;&nbsp;-- elementary.volume_threshold:
+      &nbsp;&nbsp;&nbsp;&nbsp;arguments:
+        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="/data-tests/anomaly-detection-configuration/timestamp-column"><font color="#CD7D55">timestamp_column: column name</font></a> # Required
+        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<font color="#CD7D55">warn_threshold_percent: int</font> # Optional - default: 5
+        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<font color="#CD7D55">error_threshold_percent: int</font> # Optional - default: 10
+        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<font color="#CD7D55">direction: [both | spike | drop]</font> # Optional - default: both
+        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="/data-tests/anomaly-detection-configuration/time-bucket"><font color="#CD7D55">time_bucket:</font></a> # Optional
+        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="/data-tests/anomaly-detection-configuration/time-bucket"><font color="#CD7D55">period: [hour | day | week | month]</font></a>
+        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="/data-tests/anomaly-detection-configuration/time-bucket"><font color="#CD7D55">count: int</font></a>
+        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="/data-tests/anomaly-detection-configuration/where-expression"><font color="#CD7D55">where_expression: sql expression</font></a> # Optional
+        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<font color="#CD7D55">days_back: int</font> # Optional - default: 14
+        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<font color="#CD7D55">backfill_days: int</font> # Optional - default: 2
+        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<font color="#CD7D55">min_row_count: int</font> # Optional - default: 100
+ </code>
+</pre>
+
+<RequestExample>
+
+```yml Models
+models:
+  - name: < model name >
+    data_tests:
+      - elementary.volume_threshold:
+          arguments:
+            timestamp_column: < column name > # Required
+            warn_threshold_percent: < int > # Optional - default: 5
+            error_threshold_percent: < int > # Optional - default: 10
+            direction: < both | spike | drop > # Optional - default: both
+```
+
+```yml Default thresholds (5% warn, 10% error)
+models:
+  - name: daily_orders
+    data_tests:
+      - elementary.volume_threshold:
+          arguments:
+            timestamp_column: created_at
+          config:
+            tags: ["elementary"]
+```
+
+```yml Custom thresholds
+models:
+  - name: critical_transactions
+    data_tests:
+      - elementary.volume_threshold:
+          arguments:
+            timestamp_column: transaction_time
+            warn_threshold_percent: 3
+            error_threshold_percent: 8
+            direction: drop
+          config:
+            tags: ["elementary"]
+            severity: error
+```
+
+```yml With time bucket and filter
+models:
+  - name: hourly_events
+    data_tests:
+      - elementary.volume_threshold:
+          arguments:
+            timestamp_column: event_timestamp
+            warn_threshold_percent: 10
+            error_threshold_percent: 25
+            direction: both
+            time_bucket:
+              period: hour
+              count: 1
+            where_expression: "event_type = 'purchase'"
+          config:
+            tags: ["elementary"]
+```
+
+</RequestExample>
+
+### Features
+
+- **Dual severity levels**: Separate thresholds for warnings and errors, giving you graduated alerting
+- **Directional monitoring**: Choose to monitor `both` directions, only `spike` (increases), or only `drop` (decreases)
+- **Incremental metric caching**: Uses Elementary's `data_monitoring_metrics` table to avoid recalculating row counts for previously computed time buckets
+- **Minimum baseline protection**: The `min_row_count` parameter prevents false alerts when the baseline is too small
+- **Configurable time buckets**: Works with hourly, daily, weekly, or monthly buckets
+
+### Parameters
+
+| Parameter                 | Required | Default | Description                                                                  |
+| ------------------------- | -------- | ------- | ---------------------------------------------------------------------------- |
+| `timestamp_column`        | Yes      | —       | Column to determine time periods                                             |
+| `warn_threshold_percent`  | No       | 5       | Percentage change that triggers a warning                                    |
+| `error_threshold_percent` | No       | 10      | Percentage change that triggers an error                                     |
+| `direction`               | No       | `both`  | Direction to monitor: `both`, `spike`, or `drop`                             |
+| `time_bucket`             | No       | `{period: day, count: 1}` | Time bucket configuration                              |
+| `where_expression`        | No       | —       | SQL expression to filter the data                                            |
+| `days_back`               | No       | 14      | Days of metric history to retain                                             |
+| `backfill_days`           | No       | 2       | Days to recalculate on each run                                              |
+| `min_row_count`           | No       | 100     | Minimum rows in the previous bucket required to trigger the check            |
+
+### Comparison with volume_anomalies
+
+| Feature | `volume_threshold` | `volume_anomalies` |
+| --- | --- | --- |
+| Detection method | Fixed percentage thresholds | ML-based anomaly detection |
+| Severity levels | Dual (warn + error) | Single (pass/fail) |
+| Best for | Known acceptable ranges | Unknown/variable patterns |
+| Configuration | Explicit thresholds | Sensitivity tuning |
+| Baseline | Previous bucket | Training period average |
+
+### Notes
+
+- The `warn_threshold_percent` must be less than or equal to `error_threshold_percent`
+- The test uses Elementary's metric caching infrastructure — row counts for previously computed time buckets are reused across runs
+- If the previous bucket has fewer rows than `min_row_count`, the test passes (insufficient data for a meaningful comparison)
+- The test only evaluates completed time buckets

docs/docs.json

@@ -436,7 +436,9 @@
             "group": "Other Tests",
             "pages": [
               "data-tests/python-tests",
-              "data-tests/execution-sla"
+              "data-tests/execution-sla",
+              "data-tests/data-freshness-sla",
+              "data-tests/volume-threshold"
             ]
           },
           {