Complete field-level breakdown of every metadata object, quality metric, and signal Prizm extracts from Snowflake across all platform jobs.
Prizm runs the following platform jobs against each Snowflake connector. Each job has a defined type, execution flow, and purpose. Select a job below to see the details.
Technical
Operational
Lineage
Performance
Usage Summary
Governance
Criticality Recommendation
Compute Metric
Profile Recommendation
Structural Recommendation
Profile
Structural
Asset Detail Recommendation
Business Metric Recommendation
Metric Detail Recommendation
Term Recommendation
Alert Cluster
Score Trend
Purge
Type
CONTEXT
Execution
Runs immediately after source is configured — first job in the pipeline
What this job does: Extracts accounts, warehouses, and databases from Snowflake. This is the foundation job — all subsequent jobs depend on it completing successfully. Without a successful TECHNICAL run, no other job can start.
Prizm catalogs TABLE and VIEW object types. Dynamic Tables, Stored Procedures, External Tables, and Materialized Views are not currently included in the catalog collection.
Type
CONTEXT / TRUST
Execution
Starts after TECHNICAL completes
What this job does: Extracts schemas, tables, views, and volume, freshness, and schema information for table assets from Snowflake metadata. Serves a dual purpose — it feeds the Prizm catalog (CONTEXT) and the alerting and anomaly detection pipeline (TRUST). It is the source of all time-series observability signals in Prizm and the upstream dependency for most parallel jobs.
Signal
Source
Description
Volume
INFORMATION_SCHEMA.TABLES (row_count)
Row count trend; anomaly alerts on drops/spikes
Freshness
INFORMATION_SCHEMA.TABLES (last_altered)
Time since last DDL or DML update; SLA breach alerts
Schema Change
INFORMATION_SCHEMA.TABLES (last_ddl)
Column additions, renames, type changes, removals
Anomaly detection uses adaptive forecasting models (EWMA, AutoReg, Prophet, SARIMAX) selected automatically based on each metric’s historical pattern. Thresholds are recalculated after every run. A new metric spends its first more than 5 historical collection runs in Learning mode before alerts begin firing — this threshold is counted in collection runs, not elapsed time.
Pausing and resuming a source. Freshness is computed at collection time as the interval since the asset was last altered. Pausing collection freezes the displayed freshness value at whatever it was when the pause took effect — Prizm has no way to know the asset went untouched during the pause versus simply not being checked. When collection resumes, the first run measures the entire pause duration as staleness, which typically fires a critical freshness alert on every asset at once and writes that spike into the anomaly-detection baselines, distorting future thresholds.To avoid both the false alert storm and the baseline contamination when resuming a paused source: let the source’s underlying tables receive normal activity first, then let one full collection cycle complete and land before re-enabling downstream alerting, rather than re-enabling everything the moment the source itself reconnects.
Type
CONTEXT
Execution
No scheduled run. Lineage does not appear on the schedule screen and does not fire automatically — it runs only when triggered manually from an individual asset’s run menu.
What this job does: Extracts lineage using Snowflake ACCESS_HISTORY and QUERY_HISTORY to identify object dependencies and data flow.
Lineage has no automatic schedule today. Trigger it manually per asset when you need current lineage data. Snowflake’s underlying OBJECT_DEPENDENCIES view can lag up to 3 hours behind the actual DDL change, so a manual run immediately after a schema change may not yet reflect it — wait for the latency window to pass before re-running if the lineage graph looks stale.
Run
Method
First run — view lineage
Seeded from SNOWFLAKE.ACCOUNT_USAGE.OBJECT_DEPENDENCIES
First run — table lineage
Extracted per asset using SNOWFLAKE.CORE.GET_LINEAGE
Subsequent runs (DDL change detected)
Re-extracted only for assets with DDL changes; column-level lineage included (include_attribute_lineage = true)
Tables, views, and pipelines that depend on this asset
Lineage — both table-level and column-level — requires Snowflake Enterprise edition or above, since it depends on SNOWFLAKE.CORE.GET_LINEAGE. Standard edition does not have a query-log-parsing fallback: on a Standard-edition account, the Lineage graph stays empty.
Type
CONTEXT
Execution
Runs in parallel after OPERATIONAL completes
What this job does: Extracts warehouse usage, credit consumption, and query statistics for the previous 24 hours. Powers the Usage and Performance views in the Prizm asset detail page. Requires IMPORTED PRIVILEGES on the SNOWFLAKE database.
Category
Metrics
Source
Query Performance
Execution time, queue time, compilation time, bytes scanned, bytes written, rows produced, status
ACCOUNT_USAGE.QUERY_HISTORY
Warehouse Compute
Credits used per warehouse per hour
ACCOUNT_USAGE.WAREHOUSE_METERING_HISTORY
Storage
Average bytes stored (tables, fail-safe, stage)
ACCOUNT_USAGE.DATABASE_STORAGE_USAGE_HISTORY
Data Transfer
Bytes transferred to external destinations
ACCOUNT_USAGE.DATA_TRANSFER_HISTORY
Access History
User, query, objects read/written, timestamp
ACCOUNT_USAGE.ACCESS_HISTORY
ACCOUNT_USAGE views have a data latency of up to 45 minutes. Prizm retains time-series observability data, quality score history, and query usage history for a default window of 90 days.
Type
CONTEXT
Execution
Runs in parallel after OPERATIONAL completes
What this job does: Extracts summarized usage information and generates usage-based criticality inputs using 7 days of activity. Analyses how frequently each asset is queried, by whom, and with what patterns — then feeds those signals into the CRITICALITY RECOMMENDATION job. Assets with high query volume or many unique users are more likely to be recommended as critical.
What It Produces
Description
Usage frequency
Number of queries per asset over the past 7 days
Unique user count
Number of distinct users who accessed each asset
Access patterns
Read vs write activity, peak usage windows
Criticality inputs
Usage-based signals consumed by the CRITICALITY RECOMMENDATION job
Type
CONTEXT
Execution
Runs in parallel after OPERATIONAL completes
What this job does: Extracts governance metadata including tags and business metadata. Keeps Prizm’s governance layer in sync with classifications, ownership, and tagging applied directly in Snowflake. Requires IMPORTED PRIVILEGES on the SNOWFLAKE shared database for tag ingestion.
Object
Fields Collected
Snowflake Source
Tags*(Enterprise)*
Tag name, value, associated objects
ACCOUNT_USAGE.TAGS, TAG_REFERENCES
Business metadata
Owner, description, classification fields
INFORMATION_SCHEMA object properties
Type
CONTEXT
Execution
Runs in parallel after OPERATIONAL completes
What this job does: Calculates and recommends criticality scores for assets and attributes. Criticality determines how much observability coverage an asset receives — higher criticality assets get more frequent monitoring, more structural metrics, and tighter alert thresholds. The output is a recommended criticality level (LOW, MEDIUM, HIGH, CRITICAL) per asset, which users can accept or override.
Input Signal
Source Job
Usage frequency and user count
USAGE SUMMARY
Downstream dependency count
LINEAGE
Business metadata completeness
GOVERNANCE
Asset type and schema position
TECHNICAL / OPERATIONAL
Type
TRUST
Execution
Runs in parallel after OPERATIONAL completes
What this job does: Computes non-metadata metrics such as freshness and volume for non-table assets by running direct queries against Snowflake. The OPERATIONAL job derives these signals from metadata (e.g. INFORMATION_SCHEMA.TABLES.LAST_ALTERED) — but for assets like views and external tables, metadata alone is not sufficient. COMPUTE METRIC fills this gap by executing lightweight SQL queries to obtain accurate signals.
Asset Type
What Is Computed
How
View
Row count, freshness
Direct SELECT COUNT(*) and query history lookup
External Table
Row count, last modified
Query against external stage metadata
Materialized View
Row count, refresh timestamp
Query against materialized view metadata
Type
TRUST
Execution
Runs in parallel after OPERATIONAL; PROFILE job depends on its output
What this job does: Recommends profiling schedules and configurations based on asset scope definitions. Determines which assets should be profiled, at what frequency, and with what sample settings — taking into account criticality level, asset size, and scope configuration. The PROFILE job will not run until this job has produced its output and created the schedules.
Type
TRUST
Execution
Runs in parallel after OPERATIONAL; STRUCTURAL job depends on its output
What this job does: Recommends structural quality metrics (Essential or Advanced) for each attribute. Analyses the column’s data type, profile history, and criticality to determine the appropriate metric set. Essential metrics are always recommended; Advanced metrics are added for columns where deeper analysis is warranted. The STRUCTURAL job executes against this recommendation output.
Type
TRUST
Execution
Triggered after PROFILE RECOMMENDATION completes and schedules are created
What this job does: Executes profiling jobs according to the schedules generated by PROFILE RECOMMENDATION and stores the profiling results. Can also be triggered manually from the asset page at any time.Table-level metrics:
Metric
Description
Row Count
Total rows at profile time
Duplicate Row Count
Rows that are exact duplicates of another row
Completeness Score
% of columns with non-null values across all rows
Column-level metrics:
Metric
Applicable Types
Description
Null Rate
All
% of null values
Distinct Count
All
Number of unique non-null values (cardinality)
Uniqueness Rate
All
% of values appearing exactly once
Min / Max
Numeric, Date/Timestamp
Observed range of values
Mean
Numeric
Arithmetic mean
Standard Deviation
Numeric
Statistical spread
Median
Numeric
50th percentile value
Top N Values
String, Boolean
Most frequent values and their counts
Pattern Conformance
String
% matching an expected regex format
Data Type Conformance
String
% of values parseable as the declared type
Advanced numeric statistics are computed for types containing int, number, decimal, float, or double. Snowflake types such as REAL, BIGINT, and SMALLINT still receive min, max, distinct count, and null counts.
Trigger conditions:
Trigger
Conditions
Scheduled run
Active schedule, next_run_at is due, source is valid and active
Manual run
User triggers Profile from the asset UI (TABLE, VIEW, MATERIALIZED_VIEW)
Platform mode
PROFILE job type must be enabled in platform mode
Criticality gating
Only attributes eligible for the asset’s criticality level are profiled
Skip (incremental)
Skipped if row count is unchanged since last run, or profile is newer than last_altered_on
Triggered after STRUCTURAL RECOMMENDATION completes and metrics are created
What this job does: Executes structural quality metrics according to the schedules and configurations generated by STRUCTURAL RECOMMENDATION. Computes distribution, frequency, pattern, and statistics metrics at the attribute level for all in-scope columns.
Metric Category
What Is Computed
Distribution
Null count, empty, space, distinct, repeating, character composition, leading/trailing/inner space
Frequency
Enum (value histogram), min/max value, value range, min/max length, length distribution
Pattern
Short pattern, long pattern, user-defined regular expressions
Statistics
Mean, median, mode, standard deviation, sum, Q1, Q3, range, skewness, kurtosis, variance, margin of error
Type
CONTEXT
Execution
Independent job; no upstream dependency
What this job does: Recommends asset definitions, descriptions, intended audience, semantic context, and execution scope settings (Incremental, Sampling with Filter, or None). Uses AI to analyse the asset’s name, column names, sample values, and usage patterns to generate human-readable context that data stewards can review and accept.
What It Generates
Description
Description
AI-generated plain-language description of what the asset contains and its purpose
Who should use
Recommended audience — data analysts, engineers, business users, etc.
Semantic context
Suggested domain, product, application, and tag classifications
Execution scope
Recommended profiling mode — Incremental (only changed rows), Sampling with Filter, or None
Type
TRUST
Execution
Independent job; no upstream dependency
What this job does: Recommends business metrics for assets based on column names, data types, profiling results, and industry patterns. Suggests quality checks that go beyond structural profiling — for example, a not-null check on a primary key column, or a range check on a revenue column. Recommendations appear in the Prizm UI for review before any metric is created.
Type
TRUST
Execution
Independent job; no upstream dependency
What this job does: Recommends metric descriptions, purposes, and rationale for existing metrics. For each metric already defined on an asset, this job generates a human-readable explanation of what the metric checks, why it matters, and what a failure would indicate. These descriptions appear in the Prizm metric detail panel and help data stewards understand the intent behind auto-generated checks.
Type
CONTEXT
Execution
Independent job; no upstream dependency
What this job does: Maps assets and attributes to existing glossary terms or generates new business terms where required. Analyses column names, descriptions, and sample values to find matching terms in the Prizm business glossary. Powers the Semantic layer in Prizm — ensuring that columns are aligned to approved business vocabulary.
Output
Description
Term mapping
Links a column to an existing glossary term (e.g. customer_id → “Customer Identifier”)
Term proposal
Suggests a new glossary term where no existing match is found
Confidence score
Indicates how confident the AI is in the mapping recommendation
Type
TRUST
Execution
Independent job; no upstream dependency
What this job does: Clusters and groups related alerts to reduce duplication and improve root-cause analysis. When multiple alerts fire simultaneously on the same asset or related assets — for example, a freshness alert and a volume alert on the same table — this job groups them into a single alert cluster. Reduces alert fatigue by surfacing correlated failures together rather than as separate notifications.
Type
TRUST
Execution
Independent job; no upstream dependency
What this job does: Generates and refreshes daily asset trend data by aggregating DQ scores, alerts, issues, and asset metadata. Powers the score history charts on the asset detail page — the time-series view of how an asset’s quality score has evolved over time. Also feeds domain-level and portfolio-level trend views in the Prizm Scorecard and Analytics pages.
Type
PLATFORM
Execution
Independent job; no upstream dependency
What this job does: Cleans and maintains the Prizm repository by removing obsolete data. Deletes stale metric history, expired alert records, orphaned job artifacts, and data from deleted or disconnected sources. Runs on a configurable schedule to keep the Prizm database within the configured retention window. Does not affect live assets or active metric data within the retention period.
None of the four recommendation jobs above (Asset Detail, Business Metric, Metric Detail, Term) can be generated in bulk — each produces suggestions for one asset at a time. The only bulk control available is Approve Recommendations on the asset page, which accepts already-generated suggestions for that asset’s pending recommendations; it does not create new ones. There is currently no control that generates recommendations across more than one asset at a time.
Once Prizm completes its initial catalog and observability runs, every Snowflake table and view gets a unified asset detail page. The tabs below cover what each section shows.
Overview
Attributes & Profile
Metrics
Usage
Lineage
Section
What Prizm Shows
Quality Score
Overall data quality percentage computed across all active metrics
Status
Review state (e.g., READY FOR REVIEW) and criticality badge
Key Metrics
VOLUME (row count), SCHEMA (column count), FRESHNESS (time since last update), METRICS (total quality metrics defined)
Description
AI-generated or manually verified description of the asset
Semantic Context
Domain, Application, Product, and Tag classifications
Owners
Business owner, Technical owner, and Steward contacts
Lineage Summary
One-hop upstream sources and downstream consumers
Context Panel
AI-generated narrative, context completeness score (0–100%), and freshness timestamps for description, terms, and owner verification
The Attribute tab lists every column Prizm has cataloged — data type, quality score, alert/issue counts, and the number of quality metrics defined.Clicking any attribute opens its column profile with four views: Distribution, Pattern, Custom Pattern, and Frequency.
Profile Section
What Prizm Shows
Completeness
Count and % of NULL, EMPTY, SPACE, and NON-EMPTY values
Uniqueness
Count and % of DISTINCT vs REPEATING values
Character
Distribution across DIGIT, ALPHABET, ALPHA NUMERIC, SPECIAL CHARACTER
Length
Minimum and maximum character length
Pattern
Top regex patterns detected across values
Frequency
Most and least frequent individual values
For the full breakdown of profiling views, metric types, and how to interpret profile results, see the Data Profiling section.
The Metric tab lists all quality metrics defined on the asset, grouped by context. Each row shows the metric name, column, domain, current score, alert count, issue count, and action controls.
Operational
Structural
Business
Performance
Reconciliation
Semantics
Asset-level health signals. Monitor: Yes — Score: No
Metric
Subcategory
Description
Row
Volume
Total number of rows at the time of last scan
Volume
Volume
Combined row count and data size signal
Data size
Volume
Physical size of the asset in bytes
Freshness
Freshness
Time elapsed since the asset was last updated
Last updated
Freshness
Timestamp of the most recent data load or write
Column
Schema
Current number of columns
Schema
Schema
Full column snapshot — names, types, and order
Schema name
Schema
Name of the schema the asset belongs to
Duplicates
Duplicate
Count of duplicate rows based on a defined key or all columns
User-defined rules, filters, or expressions on a specific asset
Conditional
Different logic or thresholds based on a condition
Query
SQL query returning a scalar — enables multi-table logic
Standalone
Not bound to a specific asset — used for org-wide KPIs
Behavioral
Tracks patterns over time — detects drift, not point-in-time violations
Platform-level signals. Monitor: Yes — Score: No
Metric
Level
Total queries per day
Warehouse, Database
Query success rate
Warehouse, Database
Execution time
Warehouse, Database
Job duration
Warehouse, Database
Test execution time
Warehouse, Database
Avg blocked
Warehouse, Database
Avg queue load
Warehouse, Database
Avg running
Warehouse, Database
Database storage
Database
Total credits per day
Warehouse, Database
Credits used
Warehouse, Database
Cross-source comparison metrics.
Metric Type
Description
Comparison
Compares values, row counts, or aggregates across two registered assets
Lookup
Validates column values against a reference table or allowed set
Domain and grouping level metrics.
Grouping
Description
Domain
DQ score rolled up across all assets assigned to a domain
Product
Aggregate quality and freshness across a defined data product
Application
% of assets in an application meeting quality and freshness SLOs
Tag
PII tag coverage across assets with sensitive columns
Connection
Aggregate quality score across all assets from a given Snowflake connection
The Usage tab surfaces per-asset query intelligence derived from the Performance job.
Metric
Description
Total Queries
Total number of queries that touched this asset
Average Execution Time
Mean query execution time in milliseconds
Query Success Rate
Ratio of successful queries to total queries
Total Consumed Credit
Snowflake credits consumed by queries against this asset
Unique Users
Number of distinct users who queried this asset
Unique Warehouses
Number of distinct warehouses used to query this asset
Queries can be filtered by EXPENSIVE, SLOWER, and POPULAR.
The Lineage tab renders an interactive directed graph of all upstream sources and downstream consumers. Click any node to navigate to that asset’s detail page.The Overview tab also shows a one-hop upstream/downstream summary for quick reference. For details on how lineage data is collected from Snowflake, see the Lineage tab in the job types section above.
