> ## Documentation Index > Fetch the complete documentation index at: https://docs.parable.work/llms.txt > Use this file to discover all available pages before exploring further. # Provider Quality > Query checks, statistical process control, shape, and health for Provider tables. Provider quality is a companion query catalog, not another data pool. Provider tables hold business records at `providers.*`; `providers_quality` holds measurements about those tables. Use the latest snapshot across the Workspace or the history for one Provider table: | Surface | Address | Use it for | | ----------------- | ------------------------------------------------- | --------------------------------------------------------------- | | Latest results | `providers_quality.summary.{table}` | Workspace-wide health, failing checks, SPC breaches, and shape. | | Per-table history | `providers_quality.{connector}.{table}__{suffix}` | How a measurement changed across runs. | Only main Provider tables receive quality sidecars. Identity tables, [artifacts](/protocols/sql/data-catalog/artifact-pool), and [Parable results](/protocols/sql/data-catalog/parable-pool) do not. ## Understand the measurements ### Quality checks Quality checks compare a Provider table with thresholds defined by its stream's quality configuration and schema roles. Each row represents one check on one table. | Status | Meaning | | --------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `PASS` | The measurement satisfied its threshold. | | `FAIL` | The measurement breached its threshold. | | `SKIPPED` | The check lacked a required schema role or evaluation window, or at promote time its target column was absent from the promoted table. `timestamp_coverage` and `timeline_gaps` also skip when the event-time column is not a `Date32` or `Timestamp` column. The evidence names the missing role, window, or column, or the unsupported column type. | `SKIPPED` is not a pass. It means the rule could not evaluate. Promote-time checks scan the committed Delta table with its stored column types. A reseal replays the day's promote transaction, so a day committed under an earlier plan keeps the types it was written with; a check whose target column is stored as a string skips on that stored type instead of failing the unit. Retyping a stored column takes a table restatement. `no_future_timestamp` needs to know the unit of the column it reads. A timestamp column carries its own. An integer column is compared only when its schema field declares `x-temporal-format` (`unix`, `unix_millis`, and so on); an integer `metadata_timestamp` with no declared format is `SKIPPED` with `integer metadata timestamp declares no x-temporal-format`, because seconds, milliseconds and microseconds are all plausible readings ([PARABLE-4232](https://linear.app/parable-work/issue/PARABLE-4232)). | Check ID | What it measures | | -------------------------- | --------------------------------------------------------------- | | `null_rate` | Required-column null rate against `maxRate`. | | `dupe_rate` | Duplicate rate on logical business keys against `maxRate`. | | `timestamp_coverage` | Event-time values inside the expected window against `minRate`. | | `temporal_order` | `effective_from` after `effective_to` against `maxViolations`. | | `no_future_timestamp` | Metadata timestamps in the future against `maxViolations`. | | `timeline_gaps` | Event-time gaps against `maxGapDays`. | | `string_pattern_rate` | Values matching a pattern against `maxRate`. | | `referential_integrity` | Orphan rate to a referenced column against `maxOrphanRate`. | | `row_count_not_suspicious` | Row count is not one of the configured suspicious values. | Checks run when a table is promoted and on its scheduled cadence. Read the `lifecycle` column with the result: the two evaluations cover different rows. | Lifecycle | Rows checked | What a clean duplicate check establishes | | --------- | ------------------------------------------------------------- | ----------------------------------------------------- | | `promote` | Rows carrying the promote unit's `dt` in the committed table. | No repeated key in that load when `observed = 0`. | | `cadence` | The whole committed Provider table across all `dt` values. | No repeated key across the table when `observed = 0`. | On a table that holds current state, the rows carrying a load's `dt` are the load's own rows and the rows it newly marked missing, not the whole table (see [Read row state](/protocols/sql/data-catalog/provider-pool#read-row-state)). A promote `PASS` can coexist with duplicates across loads. Use the latest `cadence` result to assess the whole table, and check `captured_at` against its latest load. A missing or older cadence result does not establish current whole-table uniqueness. Production Workspaces may have no cadence rows at all: on 2026-09-14 every published Career Certified result was a promote result ([PARABLE-3989](https://linear.app/parable-work/issue/PARABLE-3989)). Until that is resolved, a whole-table duplicate measurement needs the SQL check over the table itself. `dupe_rate` counts rows beyond the distinct combinations of the schema's key columns, divided by the total rows checked. A `PASS` means the rate satisfied `maxRate`; only an observed rate of zero establishes uniqueness. Before treating a repeated entity ID as a defect, confirm the table's record grain: an event or history table can legitimately hold several records for one entity and needs a key that identifies each record. For a query restricted to a date window, a nonzero whole-table duplicate rate does not locate the duplicates inside that window. Inspect the records in the same window and with the same key before drawing that conclusion. ### Statistical process control Statistical process control (SPC) uses a Shewhart control chart for platform-owned series such as row count and column null percentage. It complements explicit quality checks; it does not replace them. | Series status | Meaning | | ---------------------- | ------------------------------------------------------------- | | `IN_BOUNDS` | The latest point is inside the warning limits. | | `WARNING` | The latest point is outside two sigma but inside three sigma. | | `OUT_OF_BOUNDS` | The latest point is outside the three-sigma control limits. | | `INSUFFICIENT_HISTORY` | The series has fewer points than the configured minimum. | Tracked series kinds include `TABLE_ROW_COUNT`, `COLUMN_NULL_PCT`, `COLUMN_VALIDATION_FAILURE_PCT`, `COLUMN_DISTINCT_COUNT`, and `COLUMN_AVG`. ### Shape and health Shape statistics record measurements such as row count, column count, and bytes. Distinct counts skip engine-owned `_parable_*`, `_raw_*`, and `_custom_fields` columns — those payloads are not user columns, and a distinct count of the raw row is the row count. String and binary distinct counts are HyperLogLog approximations (`approx_distinct`); integer, timestamp, boolean, and decimal columns still use exact `COUNT(DISTINCT)`. `distinct_counts_exact` is false when any column used an approximation or is nested. Health is the catalog annotation for a table, including its state, reason, and watermark. Health and the quality/SPC rollups are related views, but they are computed through different paths. ## Query the latest Workspace snapshot These summary tables contain the current result per Provider table: | Table | Contents | | ------------------------------------------ | --------------------------------------------- | | `providers_quality.summary.quality_checks` | Latest quality check per table and lifecycle. | | `providers_quality.summary.spc_results` | Latest SPC series per table. | | `providers_quality.summary.table_stats` | Latest row, column, and byte counts. | | `providers_quality.summary.table_health` | Health state, reason, and watermark. | Failing checks for Google Workspace data: ```sql theme={null} SELECT connector, table_name, lifecycle, check_id, check_status, observed, threshold, comparator, captured_at FROM providers_quality.summary.quality_checks WHERE connector = 'google' AND check_status = 'FAIL' ORDER BY captured_at DESC ``` Every check that did not pass: ```sql theme={null} SELECT connector, table_name, lifecycle, check_id, check_status, observed, threshold FROM providers_quality.summary.quality_checks WHERE check_status IN ('FAIL', 'SKIPPED') ORDER BY connector, table_name, lifecycle, check_id ``` Whole-table duplicate measurements for the Workspace: ```sql theme={null} SELECT connector, table_name, check_status, observed, threshold, captured_at FROM providers_quality.summary.quality_checks WHERE lifecycle = 'cadence' AND check_id = 'dupe_rate' ORDER BY connector, table_name ``` This query returns only tables with a cadence result. Check the per-table history and load status when an expected table has no row. SPC series with a warning, control-limit breach, or recorded violation: ```sql theme={null} SELECT connector, table_name, series_id, series_kind, latest_value, mean, stddev, series_status, overall_status, captured_at FROM providers_quality.summary.spc_results WHERE has_violations = true OR series_status IN ('WARNING', 'OUT_OF_BOUNDS') ORDER BY captured_at DESC ``` Largest Provider tables by row count: ```sql theme={null} SELECT connector, table_name, row_count, column_count, total_byte_size, captured_at FROM providers_quality.summary.table_stats ORDER BY row_count DESC LIMIT 20 ``` Provider tables with stale watermarks: ```sql theme={null} SELECT connector, table_name, health_state, health_reason, watermark, watermark_stale, computed_at FROM providers_quality.summary.table_health WHERE watermark_stale = true ``` ## Query one table's history History suffixes map to result kinds: | Suffix | History of | | ---------- | ------------------------------------------------------------------- | | `__checks` | Quality checks, one row per check per run. | | `__spc` | SPC series, one row per series per run. | | `__stats` | Shape statistics. | | `__runs` | Maintenance runs such as compaction and vacuum, not quality scores. | Recent checks for Google Workspace directory users: ```sql theme={null} SELECT captured_at, check_id, check_status, observed, threshold, lifecycle, overall_status FROM providers_quality.google.users__checks ORDER BY captured_at DESC LIMIT 20 ``` Row-count SPC over time for the same table: ```sql theme={null} SELECT captured_at, series_id, series_kind, latest_value, mean, upper_control3_sigma, lower_control3_sigma, series_status FROM providers_quality.google.users__spc WHERE series_kind = 'TABLE_ROW_COUNT' ORDER BY captured_at DESC LIMIT 20 ``` History tables retain each run. They can also expose `result_id`, `run_id`, `evidence_json` for checks, and `violations_json` for SPC when an application needs the payload behind a status. Older sidecar records can use statuses such as `Fail` or `OutOfBounds`; newer records use `FAIL` and `OUT_OF_BOUNDS`. Summary tables use typed decoding and return uppercase values. If a history query unexpectedly returns no rows, inspect the available values before choosing a casing-sensitive filter.