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Creating a comparison metric

1

Add the metric

  1. Navigate to Metric in the top navigation.
  2. Click the Add button (⊕).
  3. Enter a Metric Name.
  4. Select Subcategory: Comparison from the dropdown.
  5. Enter a Purpose description.
  6. Click Create.
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2

Open the configuration view

Click the metric name to open its detail page. The comparison metric configuration view exposes:
  • Source A — the primary dataset
  • Source B — the reference dataset
  • Metadata tabs — Description, Purpose, Rationale
  • Comparison Metric Configuration — comparison mode, alignment, metric expressions, and schema mapping
  • Threshold
  • Semantic context and Owners
Comparison Configuration View
3

Select Source A and Source B

Select the two datasets to compare. Each source is configured independently.Source A is the primary dataset — typically the upstream or source-of-truth system. Source B is the reference dataset — the downstream target, replicated system, or transformed output.For each source, select:
  • Connector — the data source connection
  • Table / View — the specific asset to query
Source A and Source B do not need to use the same data platform. Cross-cloud comparisons (e.g., Snowflake vs. Databricks, or a dbt model output vs. a raw source table) are fully supported.
Comparison Source Selection
4

Choose the comparison mode

Select how Prizm compares the two sources:
Comparison Mode Selection
5

Set alignment and schema mapping

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Time and partition alignment

Configure how records are aligned between the two sources before comparison. Misalignment (e.g., comparing a stale target against a fresh source) causes false positives.Prizm includes a freshness gate — if the target source has not been updated within the expected window, the comparison is skipped and no alert is raised, preventing stale-data false positives.

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Schema mapping

For Profile-to-Profile and Row/Value-Level Diff modes, map columns between the two sources:
Comparison Alignment and Mapping
6

Define the metric expressions (Configuration tab)

For Metric-to-Metric mode, define the metric value to compute from each source and how to evaluate the difference.

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Aggregate expressions

Write the aggregate expression for each source. Common templates:Prizm AI can auto-suggest appropriate expressions based on the metric name, purpose, and the schemas of both sources.

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Comparison formula

Choose how to evaluate the difference between the two computed values:

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Rule options

Click Validate to run a sample comparison and preview the A value, B value, and computed difference before saving.
Comparison Metric Expressions
7

Set the threshold

Choose how Prizm determines when to raise an alert on the comparison result:Automated Threshold — Prizm learns statistical baselines from historical deviation values and alerts when the result deviates by a configurable number of standard deviations:Custom Threshold — Set explicit tolerance bounds:Noise guards prevent false positives:
Comparison Threshold
8

Set semantic context and owners

Scroll down past the Threshold section to complete:
  • Semantic context: Assign Domain, Application, Product, Tag, and Dimension to make the metric discoverable and contextually relevant.
  • Owners: Set Business owner, Technical owner, and Steward.
  • Audience: Restrict who can view this metric’s results.
Click Save Changes when done.