SQL Server Overview

Microsoft SQL Server is a widely used relational database system deployed on-premises, in private data centers, and on Azure. Once connected, Prizm continuously monitors your SQL Server tables for quality anomalies, tracks schema and volume changes, builds lineage graphs from foreign key relationships and stored procedure dependencies, and surfaces performance insights — all without writing pipelines or custom queries.

Why Connect SQL Server to Prizm?

Connecting SQL Server gives Prizm access to three layers of intelligence: Catalog & Context Prizm discovers every database, schema, table, view, stored procedure, and column in your SQL Server instance and indexes them in the Prizm catalog. Descriptions sourced from MS_Description extended properties are imported automatically. Ownership, classifications, and custom metadata can be managed in Prizm. Data Quality & Profiling Prizm runs profile scans on your tables to compute null rates, cardinality, min/max, distribution, and completeness scores at the column level. Quality scores are tracked over time so you can see trends and catch degradation before it reaches consumers. Observability Prizm monitors every in-scope table for freshness (time since last user update via sys.dm_db_index_usage_stats), volume (row count changes from sys.partitions), and schema drift (added, removed, or renamed columns). Machine-learning anomaly detection sets adaptive thresholds so alerts fire on real deviations — not noise.

Metric Support

Every metric in Prizm belongs to a context — the stakeholder lens that defines who the metric serves and why it matters. The following table provides the list of all supported metrics.

Operational metrics monitor the day-to-day health of data assets — whether data is arriving on time, in the expected volume, and with the correct structure. They run at the Asset level and are the primary driver of alerting and anomaly detection in Prizm.

All Operational metrics feed directly into the Alerts dashboard. They are the most actively monitored metrics in a typical Prizm deployment, with Freshness and Volume generating the highest alert volumes in production.

Metric	Subcategory	Dimension	Level	Monitor	Description
Row	Volume	Completeness	Asset	Yes	Total number of rows at the time of last scan. Tracks volume over time and triggers alerts on drops or spikes.
Volume	Volume	Completeness	Asset	Yes	Higher-level measure combining row count and data size signals. Used to detect significant changes in the amount of data arriving.
Data size	Volume	Completeness	Asset	Yes	Physical size of the asset in bytes (from `sys.allocation_units`). Complements row count — a large size increase with no row increase may indicate wide or bloated records.
Freshness	Freshness	Availability	Asset	Yes	Time elapsed since the asset was last updated (from `sys.dm_db_index_usage_stats.last_user_update`). Fires an alert when data has not been refreshed within the expected window.
Last updated	Freshness	Timeliness	Asset	Yes	Timestamp of the most recent data write. Verifies pipelines are running on schedule and consumers are not working with stale records.
Column	Schema	Validity	Asset	Yes	Current number of columns. Detects column additions or removals indicating an unannounced schema change at the source.
Schema	Schema	Validity	Asset	Yes	Snapshot of the full column schema — names, types, and order. Alerts when any column name or type changes, which can silently break downstream transformations.
Schema name	Schema	Validity	Asset	Yes	Name of the schema the asset belongs to. Tracks unexpected schema renames that would break hard-coded references.
Duplicates	Duplicate	Uniqueness	—	Yes	Count of duplicate rows based on a defined key or all columns. Unexpected duplicates often indicate a pipeline re-run without deduplication or broken merge logic.

Operational metrics do not contribute to trust scoring (Score: No). They are observability signals — designed to trigger alerts, not to roll up into a quality score.

Performance metrics measure how efficiently the data platform itself is running — query execution time, session counts, and query volume. They operate at the Source and Database level rather than the individual asset level.

Metric	Level	Profile Type	Description
Total queries per day	Database	Performance	Total queries executed per day (from `sys.dm_exec_query_stats`). Tracks usage trends and surfaces unexpected spikes.
Query success rate	Database	Performance	Percentage of queries completing without error. A falling rate signals pipeline instability or permission issues.
Execution time	Database	Performance	Average query execution time. Rising times indicate missing indexes or poorly optimized SQL.
Session count	Database	Performance	Number of active sessions (from `sys.dm_exec_sessions`). Spikes indicate unusual concurrency or runaway processes.
Database storage	Database	Performance	Total storage consumed by the database including tables, indexes, and transaction logs. Tracks growth trends.

Performance metrics do not contribute to trust scoring (Score: No). They are platform observability signals for infrastructure and database administrators.

Structural metrics profile the internal shape of data at the column level — how values are distributed, how often patterns repeat, and what the numbers say. All structural metrics are auto-generated when an asset is profiled — no configuration required.Distribution — How values are composed and spread:

Metric	Subcategory	Dimension	Profile Type	Description
Null count	Completeness	Completeness	Essential	Total null values. High null count signals missing data that may break downstream logic.
Empty	Completeness	Completeness	Essential	Count of empty strings (`""`). Distinct from null — present but containing no meaningful content.
Non-empty	Completeness	Completeness	Advanced	Count of values that are neither null nor empty.
Space	Completeness	Completeness	Essential	Values containing only whitespace.
Zero value	Numeric	Completeness	Essential	Numeric values equal to zero. Flags columns where zero may be a placeholder for missing data.
Distinct	Uniqueness	Uniqueness	Essential	Count of unique values.
Repeating	Uniqueness	Uniqueness	Advanced	Count of values appearing more than once.
Leading space	Space	Validity	Advanced	Values with spaces at the start. Can cause join failures and mismatches in downstream queries.
Trailing space	Space	Validity	Advanced	Values with spaces at the end.
Special character	Character	Validity	Advanced	Values containing at least one special character.

Frequency — How often values, lengths, and ranges repeat:

Metric	Subcategory	Dimension	Profile Type	Description
Enum	Value	Validity	Advanced	Distinct values and their frequencies. Reveals category distribution and flags values outside an expected allowed list.
Min value	Value	Validity	Essential	Smallest value in the column.
Max value	Value	Validity	Essential	Largest value in the column.
Min length	Length	Validity	Essential	Shortest string length found. Catches truncated values.
Max length	Length	Validity	Essential	Longest string length found.

Pattern — Structural format templates:

Metric	Subcategory	Dimension	Profile Type	Description
Short pattern	Pattern	Validity	Advanced	Condensed format — collapses consecutive identical character types into a single token.
Long pattern	Pattern	Validity	Advanced	Character-by-character format — preserves full length and composition.
Regular expressions	Pattern	Validity	Essential	User-defined regex patterns to validate that column values conform to a known format.

Statistics — Numeric summaries:

Metric	Subcategory	Dimension	Profile Type	Description
Mean	Descriptive	Accuracy	Advanced	Arithmetic average of all non-null values.
Median	Descriptive	Accuracy	Advanced	Middle value when sorted.
Standard deviation	Descriptive	Accuracy	Advanced	Spread of values around the mean.
Sum	Descriptive	Accuracy	Advanced	Total of all non-null numeric values.
Variance	Shape	Accuracy	Advanced	Average of squared differences from the mean.

Business metrics are user-defined measurements aligned to domain rules, KPIs, and data contracts. Unlike structural and operational metrics which are auto-generated, business metrics are created by analysts, engineers, and domain SMEs to capture logic the platform cannot infer automatically.

Type	Description
Custom	User-defined metric applied to a specific asset using filters, expressions, or threshold rules.
Conditional	Metric applying different logic or thresholds depending on another column’s value.
Query	Metric defined by a SQL query returning a scalar value. Enables joins, aggregations, and CTEs.
Standalone	Metric not bound to a specific asset. Used for org-wide KPIs or cross-domain aggregations.
Behavioral	Metric evaluating patterns over time rather than a single point. Detects trend changes or gradual drift.

Reconciliation metrics compare data across sources, snapshots, or reference tables to verify consistency and correctness. Critical for cross-system data contracts, ETL validation, and regulatory compliance.

Type	Description
Comparison	Compares a column or aggregate value between two registered assets — e.g. row count in a source table versus the same table after an ETL load.
Lookup	Validates that every value in a column exists in a reference dataset or allowed set. Used for referential integrity checks.

Comparison metrics require both source and target assets to be registered and accessible in Prizm. If a source asset is unreachable, the metric shows Score: NA and raises an availability alert.

Semantic metrics are derived at a logical or business grouping level — domain, product, application, tag, or connection — rather than at the individual asset level. They aggregate quality signals across ownership boundaries to enable domain- and product-level KPIs and SLOs.

Grouping	Example Metric	Description
Domain	DQ score for Finance domain	Weighted quality score rolled up across all assets assigned to a domain
Product	Data product health score	Aggregate quality and freshness across all assets in a defined data product
Connection	Source reliability score	Aggregate quality score across all assets from a given SQL Server connection

Catalog & Lineage Support

Category	Capability	Support
Catalog	Metadata discovery (databases, schemas, tables, views, columns)	✅
Catalog	Stored procedure discovery	✅
Catalog	MS_Description import (existing column/table descriptions)	✅
Lineage	Table-level lineage (via FK constraints)	✅
Lineage	View-level lineage (via `sys.dm_sql_referenced_entities`)	✅
Lineage	Stored procedure lineage	✅
Lineage	Column-level lineage	✅

Supported SQL Server Objects

Object	Catalog	Quality	Observability	Lineage
Table	✅	✅	✅	✅
View	✅	—	Schema only	✅
Stored Procedure	✅	—	—	✅

Supported Deployment Modes

Deployment	Supported
On-premises SQL Server	✅
Self-hosted VM	✅
Azure SQL Database	✅
Azure SQL Managed Instance	✅

For air-gapped or private network deployments, use the Prizm Outbound Agent with the SQL Server connector. The agent runs inside your network and connects outbound to Prizm over WSS port 443 — no inbound firewall rules required.

Next Steps

Setup

Connect SQL Server to Prizm — prerequisites, authentication, and configuration.

What We Collect

Full field-level breakdown of every metadata object Prizm extracts from SQL Server.

FAQ

Common questions about the SQL Server connector.

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Why Connect SQL Server to Prizm?

Metric Support

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​Why Connect SQL Server to Prizm?

​Metric Support

​Catalog & Lineage Support

​Supported SQL Server Objects

​Supported Deployment Modes

​Next Steps

Setup

What We Collect

FAQ

Snowflake Connector

Why Connect SQL Server to Prizm?

Metric Support

Catalog & Lineage Support

Supported SQL Server Objects

Supported Deployment Modes

Next Steps