omni-model-builder

Omni Model Builder

Create and modify Omni's semantic model through the YAML API — views, topics, dimensions, measures, relationships, and query views.

Tip: Always use omni-model-explorer first to understand the existing model.

Prerequisites

# Verify the Omni CLI is installed — if not, ask the user to install it
# See: https://github.com/exploreomni/cli#readme
command -v omni >/dev/null || echo "ERROR: Omni CLI is not installed."

# Show available profiles and select the appropriate one
omni config show
# If multiple profiles exist, ask the user which to use, then switch:
omni config use <profile-name>

You need Modeler or Connection Admin permissions. Add -o json to any command to force structured output for parsing (default auto is human in a TTY, JSON when piped).

Omni's Layered Modeling Architecture

Omni uses a layered approach where each layer builds on top of the previous:

1. Schema Layer — Auto-generated from your database. Reflects tables, views, columns, and their types. Kept in sync via schema refresh.
2. Shared Model Layer — Your governed semantic model. Where you define dimensions, measures, joins, and topics that are reusable across the organization.
3. Workbook Model Layer — Ad hoc extensions within individual workbooks. Used for experimental fields before promotion to shared model.
4. Branch Layer — Intermediate development layer. Used when working in branches before merging changes to shared model.

Key concept: The schema layer is the source of truth for table/column structure (refreshed when the database changes); all user-created content (dimensions, measures, relationships, topics) flows through the shared model layer. You build and modify it in branches (see "Safe Development Workflow" below) before merging back to the shared model.

Determine SQL Dialect

Before writing any SQL expressions, confirm the dialect from the connection — don't guess from the connection name:

# 1. List models to find connectionId
omni models list

# 2. Look up the connection's dialect
omni connections list
# → find your connectionId and read the "dialect" field
# → e.g. "bigquery", "postgres", "snowflake", "databricks"

Use dialect-appropriate functions in your SQL (e.g. SAFE_DIVIDE for BigQuery, NULLIF(a/b) for Postgres/Snowflake).

Schema Refresh: Syncing with Database Changes

The schema layer is auto-generated from your database. When your database schema changes (new/deleted/renamed columns, type changes), refresh it to stay in sync: omni models refresh <modelId> (add --branch-id <branchId> to scope to a branch; requires Connection Admin).

See references/schema-refresh.md for when to trigger, what it does and its side effects, the deleted/renamed-column impact-check workflow, and connection/credential error handling.

Known Issues & Safe Defaults

• Do not merge without explicit confirmation — after branch validation and query testing, stop and ask the user before omni models merge-branch, even when the model is not git-connected. Treat requests like "add a field" or "create a view" as requests to prepare validated branch changes, not as permission to ship to production.
• Keep eval-created files on branches until confirmed — if you create fields/views for validation, report the branch name/id, validation status, and test query result. Only merge after the user explicitly says to merge, ship, publish, or promote.

Discovering Commands

omni models --help              # List all model operations
omni models yaml-create --help  # Show flags for writing YAML

Safe Development Workflow

Always work in a branch. Never write directly to production.

Step 0: Create a Branch

omni models create-branch <modelId> --name "my-feature-branch"

The response model.id is your branchId — a UUID you'll pass to all subsequent API calls. To list existing branches at any time:

omni models list --include activeBranches

Git-connected models: If your model is connected to a git repo, prefer pushing branch changes through a pull request (Step 3 below) rather than merging directly. Choose one workflow and stick to it — either edit via the Omni branch API (then git pull to sync local files), or edit local files and push via git. Mixing both leads to conflicts.

Step 1: Write YAML to a Branch

omni models yaml-create <modelId> --body '{
  "fileName": "my_new_view.view",
  "yaml": "dimensions:\n  order_id:\n    primary_key: true\n  status:\n    label: Order Status\nmeasures:\n  count:\n    aggregate_type: count",
  "mode": "extension",
  "branchId": "{branchId}",
  "commitMessage": "Add my_new_view with status dimension and count measure"
}'

Note: The branchId parameter must be a UUID from the server (Step 0). Passing a string name instead will return 400 Bad Request: Unrecognized key: "branchName".

⚠️ Editing an existing file? fileName is its exact path, not a regex (unlike on read). Reuse the full-path key from your yaml-get response verbatim, including any folder prefix — e.g. MARTS/fct_ai_events.view, not fct_ai_events.view. A non-matching fileName doesn't error: Omni silently creates a new file at that path and returns success: true, so shortening the key produces a duplicate view at the repo root.

Edits are whole-file writes — read-modify-write. yaml-create replaces a file's authored content; it does not merge field-by-field. To change or add one field on an existing view, yaml-get the file first, edit it, and write the complete file back — otherwise the other authored fields are dropped. (Schema base columns are unaffected — they live in the schema layer, not the authored file.)

Inspect a branch. yaml-get <modelId> --branchid <branchId> without --filename enumerates the whole model — --mode extension returns only the files the branch changed (your deltas); --mode combined returns the full composed model (schema + shared + branch). Then drill into any file by its exact path.

Step 2: Validate and Test

Every YAML write must be validated and tested before merging — a field can be valid YAML yet produce wrong results or broken queries.

# 1. Validate — any issue with is_warning:false is a blocking error; fix before proceeding
omni models validate <modelId> --branchid <branchId>

# 2. Query the fields you changed — confirm no error and summary.row_count > 0
omni query run --body '{"query":{"modelId":"<modelId>","table":"your_view","fields":["your_view.new_dimension","your_view.new_measure"],"limit":10,"join_paths_from_topic_name":"your_topic"},"branchId":"<branchId>"}'

# 3. Read it back — confirm the field is present (and not duplicated at a second path)
omni models yaml-get <modelId> --filename your_view.view --branchid <branchId>

Spot-check that values look right (a sum isn't returning a count; booleans read true/false), and if a field references another view include fields from both to confirm the join resolves. See references/validation-and-testing.md for join-path testing, natural-language validation via omni ai job-submit, the full results checklist, and duplicate-file recovery.

Step 3: Ship the Branch

Important: Always ask the user for confirmation before shipping. Changes applied to the production model cannot be easily undone. Only ship after validation and testing pass (Step 2).

Check whether the model is git-connected — omni models git-get <modelId>. A config with sshUrl / baseBranch → git-connected → Path A (open/update a PR); a 404/no config → not git-connected → Path B (merge directly in Omni).

Path A — Git-connected: open or update a PR

Push the branch contents to git. Creates a new git branch + PR if one doesn't exist; otherwise updates the existing PR:

omni models commit <modelId> --body '{
  "branch_id": "<branchId>",
  "commit_message": "Add my_new_view with status dimension and count measure"
}'

Surface the returned pr_url to the user. The reviewer merges the PR in your git host; changes flow back to baseBranch on the next sync. Run omni models commit --help for optional body flags (allow_branch_exists, require_branch_exists) when you need to enforce open-only or update-only behavior.

Path B — Not git-connected: merge in Omni

omni models merge-branch <modelId> <branchName>

After merging, run one final validation against the production model to confirm the merge didn't introduce conflicts:

omni models validate <modelId>

YAML File Types

Type	Extension	Purpose
View	.view	Dimensions, measures, filters for a table
Topic	.topic	Joins views into a queryable unit
Relationships	(special)	Global join definitions

Write with mode: "extension" (shared model layer). To delete a file, send empty yaml.

Writing Views

Every view that participates in joins MUST have a real primary_key: true dimension. Without a genuine row-unique primary key, queries that join to this view can produce fanout errors or incorrect aggregations. Use the table's natural unique identifier (e.g., id, order_id, user_id). If no single column is unique, build a composite key from row-level columns that are jointly unique, for example sql: ${order_id} || '-' || ${line_number}. If you cannot define a row-unique expression, do not mark a dimension as primary_key: true yet; fix the grain first or avoid joining the view until a real key exists.

Basic View

dimensions:
  order_id:
    primary_key: true
  status:
    label: Order Status
  created_at:
    label: Created Date
measures:
  count:
    aggregate_type: count
  total_revenue:
    sql: ${sale_price}
    aggregate_type: sum
    format: currency_2

Understanding Schema Layer vs Extension Layer

When you create a view, Omni separates schema (database structure) from model (your business logic):

• Schema layer: Auto-generated base dimensions, one per column. Types come from the database. Read-only, synced via schema refresh.
• Extension layer: Your custom YAML. Can override base dimensions, add new dimensions/measures, hide columns, add business logic.

When both layers exist for a field with the same name, your extension definition wins but type information comes from the schema layer.

Example: Table has columns created_at (DATE) and revenue (NUMERIC).

# Schema layer (auto-generated)
dimensions:
  created_at: {}  # type: DATE, auto-generates timeframes
  revenue: {}     # type: NUMERIC

# Extension layer (your YAML)
dimensions:
  created_at:
    label: "Order Created"
    description: "When the order was placed"

  revenue:
    hidden: true  # Hide the raw column

measures:
  total_revenue:
    sql: ${revenue}
    aggregate_type: sum
    format: currency_2

Result: created_at inherits its type from the schema layer (DATE with automatic week/month/year granularities) but gets your label. The raw revenue column is hidden, only exposed through the total_revenue measure.

Key insight: If your extension defines a dimension but there's no schema layer base dimension to provide type information, Omni can't infer granularities or types. Trigger a schema refresh to auto-generate the schema layer first.

Reading back what you wrote — --mode. yaml-get returns your extension layer by default — just the deltas you authored, not the auto-generated base columns. To see the fully-composed result (schema base + your extension merged), read with --mode combined:

# What you authored (deltas only) — default
omni models yaml-get <modelId> --filename your_view.view --branchid <branchId> --mode extension

# What the model actually resolves to (schema + extension merged)
omni models yaml-get <modelId> --filename your_view.view --branchid <branchId> --mode combined

Use extension to confirm what you changed, and combined to confirm what the model resolves to. When the model is git-integrated, the combined output mirrors what's written to the repository — which is why committed *.view.yaml files carry the schema-layer table_name: and base columns, while the extension layer holds only your deltas. (Other --mode values: staged, merged, history.)

Dimension Parameters

See references/modelParameters.md for the complete list of 35+ dimension parameters, format values, and timeframes.

Most common parameters:

• sql — SQL expression using ${field_name} references. Reference other fields with ${field} / ${view.field}; a raw column auto-maps by name (no sql:). There is no ${TABLE} construct — ${TABLE}.column errors with Column "__omni_scoped" not found at validation and query time.
• label — display name · description — help text (also used by Blobby)
• primary_key: true — unique key (critical for aggregations)
• hidden: true — hides from picker, still usable in SQL
• format — number_2, currency_2, percent_2, id
• group_label — groups fields in the picker
• synonyms — alternative names for AI matching (e.g., [client, account, buyer])

Measure Parameters

See references/modelParameters.md for the complete list of 24+ measure parameters and all 13 aggregate types.

Measure filters restrict rows before aggregation using the YAML filter condition syntax. See references/yaml-filter-syntax.md for the complete operator reference and measure filter examples.

Prefer a measure filters: block for filtered aggregates instead of embedding filter logic in sql with CASE WHEN or a SQL WHERE clause. Keep sql focused on the value being aggregated:

measures:
  completed_revenue:
    sql: ${sale_price}
    aggregate_type: sum
    filters:
      status:
        is: Complete

Cross-View Fields in Views

Avoid defining cross-view fields (dimensions or measures whose sql references ${other_view.field}) directly in a view file. These fields depend on another view being joined, which is not guaranteed in every topic that includes this view. In topics where the referenced view isn't present, the field will be omitted — but more importantly, the model validator will throw errors for any topic that includes this view without also joining the referenced view. This can create a cascade of validator errors across topics that are otherwise valid but happen to include only a subset of the involved views.

In the vast majority of cases, cross-view fields should be defined in the topic's views: block (see "Topic-Scoped View Definitions"), where the join context is explicit and controlled.

Only define a cross-view field in the view file itself when you are certain the referenced view will always be joined in every topic that includes this view — for example, when the join is defined globally and the two views are inseparable by design.

Fallback: View Missing from yaml-get

Before concluding that a view doesn't exist, always run this two-step check. yaml-get only returns views from currently-loaded schemas — views in offloaded or inactive schemas won't appear, but they're still available.

# 1. List all schemas the connection knows about (loaded, offloaded, and inactive)
omni models get-schemas <modelId>
# → {"schemas": ["ANALYTICS", "PUBLIC", "STAGING", ...]}

# 2. If the target schema appears in the list, load it explicitly
omni models yaml-get <modelId> --includeschemas PUBLIC

Rules for --includeschemas:

• Accepts exactly one schema name per call — commas are rejected. Load schemas one at a time.
• The response will contain only views from that schema; relationships to other schemas are preserved.
• To scope to a branch, add --branchid <id> to yaml-get or --branch-id <id> to get-schemas (flag names differ per command).

If the schema isn't in the get-schemas list at all, the connection likely doesn't have access or the schema isn't synced — check with a Connection Admin.

Writing Topics

Before writing a topic, verify all views you plan to reference actually exist. Run omni models yaml-get <modelId> and confirm each view appears. If a view is missing, run the lazy-load fallback above before concluding it doesn't exist — it may simply be in an offloaded schema.

New topic vs extend an existing one

When a query can't be answered by an existing topic, first check whether you should simply extend one rather than create a new one. Extending is usually right when the request's base view (the FROM) matches an existing topic's base view — e.g. add a relationship/join so a needed view becomes reachable, or add a field/label. Create a new topic when any of these is fundamentally different:

• Subject / object — a different base view (FROM). What is the query fundamentally describing — orders? users? time (dates)? A different core entity warrants its own topic.
• Constraints — conditions that are always applied (e.g. an always_where that excludes test users from order data). Different always-on filters → a different topic.
• Audience — the same fields but different labels/terminology for a different consumer; jargon that differs by audience justifies a separate topic.

Prompt the requestor when it's a judgment call, and build new topics on a branch (see the Safe Development Workflow above). Note that querying on a topic (vs a bare base view) is also what makes the result accessible to restricted queriers/viewers.

See Topics setup for complete YAML examples with joins, fields, and ai_context, and Topic parameters for all available options.

Key topic elements:

• base_view — the primary view for this topic
• joins — nested structure for join chains (e.g., users: {} or inventory_items: { products: {} })
• ai_context — guides Blobby's field mapping (e.g., "Map 'revenue' → total_revenue")
• default_filters — applied to all queries unless removed
• always_where_sql — non-removable WHERE filter using a SQL expression (cannot be removed by users)
• always_where_filters — non-removable WHERE filter using filter specifications (cannot be removed by users)
• always_having_sql — non-removable HAVING filter using a SQL expression, applied after aggregation (cannot be removed by users)
• always_having_filters — non-removable HAVING filter using filter specifications, applied after aggregation (cannot be removed by users)
• fields — field curation: [order_items.*, users.name, -users.internal_id]

Filter Expressions for Topics

When configuring default_filters, always_where_filters, or always_having_filters on a topic, use the YAML filter condition syntax — the same syntax used in measure filters. See references/yaml-filter-syntax.md for the complete reference.

If the right filter configuration for a given use case isn't obvious, use the Omni AI CLI to search the docs:

omni ai search-omni-docs "how do I configure always_where_filters on a topic in Omni?"

Use targeted questions to get precise YAML examples for your specific filtering need before writing the model YAML.

Writing Relationships

Global Relationships

Global relationships are defined in the shared relationships file and are available across all topics. Use these for standard, reusable joins.

- join_from_view: order_items
  join_to_view: users
  on_sql: ${order_items.user_id} = ${users.id}
  relationship_type: many_to_one
  join_type: always_left

Type	When to Use
many_to_one	Orders → Users
one_to_many	Users → Orders
one_to_one	Users → User Settings
many_to_many	Tags ↔ Products (rare)

Getting relationship_type right prevents fanout and symmetric aggregate errors.

Topic-Scoped Relationships

Before defining, check the global relationships file for a join between the same two views in either direction. Same on_sql → redundant, use joins: only. Different on_sql → default to the extended views pattern below rather than a silent override. Confirm intent with the modeler.

Use topic-scoped relationships for one-off joins not in the shared model, or joining the same table multiple times under different conditions.

# .topic file
relationships:
  - join_from_view: order_items
    join_to_view: users
    on_sql: ${order_items.user_id} = ${users.id}
    relationship_type: many_to_one
    join_type: always_left

joins:
  users: {}

joins vs relationships: joins declares which views are in the topic and their hierarchy; relationships defines the join conditions. A topic using only global relationships needs only joins. A topic with a one-off join needs both.

Extended Views: Joining the Same Table Multiple Ways

When the same table needs multiple joins (e.g., users as buyer and seller), use the extended views pattern — not join_to_view_as. Two variants:

Variant 1 — Global (reusable): Create a standalone .view file with extends:, a role-descriptive name, and a description:. Define the relationship globally — any topic can then join it like any other view.

Variant 2 — Topic-scoped (inline): Define the alias in the topic's views: block with its relationship in the same file. Use when the alias is not generally applicable in other topics.

See references/topic-scoped-relationships.md for full YAML examples of both variants.

If you see a relationship alias duplicates view name error, this pattern is the fix.

Topic-Scoped View Definitions

Topics can define or override views inline using a views: block — controlling display_order, overriding label, adding topic-specific filtered measures or derived dimensions, defining cross-view fields, and joining the same view multiple ways with per-alias conditions.

Before adding any topic-scoped field to an existing view:

1. Read the view YAML (omni models yaml-get) and confirm the field doesn't already exist. If it does with the same definition, skip it.
2. If a field with the same name exists but uses different SQL, this is an override. Confirm explicitly with the modeler — queries through this topic will use the topic-scoped definition; all other topics keep the shared one.

# Example: display order + topic-specific filtered measure
views:
  order_items:
    display_order: 0
    measures:
      us_revenue:
        sql: ${sale_price}
        aggregate_type: sum
        format: currency_2
        filters:
          users.country:
            is: US

See references/topic-scoped-views.md for a full pattern gallery (label overrides, derived dimensions, cross-view fields, multi-join lifecycle, topic-scoped query views).

Cross-view fields in views: blocks: Before writing ${view_name.field_name} references, confirm every referenced view is declared in the topic's joins: block — the model validator throws errors for any reference to a view that isn't joined.

Joining the same view multiple ways (e.g., ARR at Start / Current / End): Use extends: inside the topic's views: block to create named aliases, each with its own on_sql in relationships:. Each alias inherits all base view fields and can override labels independently. For a full YAML example, see references/topic-scoped-views.md.

Topic-scoped query views: A query view can also be defined inside a topic's views: block, scoping it to that topic only. Same primary key rules apply (primary_key: true or custom_compound_primary_key_sql). Include a relationships: entry and a joins: entry for the new view — see Query Views section above, and references/topic-scoped-views.md for a complete example.

Query Views

Virtual tables defined by a saved query. A query view must have a primary key or it cannot be joined without producing fanout errors. Before writing, confirm which field uniquely identifies each row — unless the primary key can be clearly inferred from the query itself and the involved views (e.g. a query that selects user_id from a users view where user_id is the known primary key).

There are two ways to define the primary key:

Option 1 — Single unique field: Mark exactly one dimension primary_key: true in the dimensions: block.

Option 2 — Compound key: When no single field is unique but a combination is, set custom_compound_primary_key_sql: [field_a, field_b] at the view level — no primary_key: true dimension needed.

Both options work with either a query: block (field-mapped virtual table) or a sql: block (raw SELECT). In sql: blocks, use ${view_name} to reference a view's underlying table rather than a hard-coded CATALOG.SCHEMA.TABLE path — it's preferred and stays correct if the table moves. See references/query-view-examples.md for complete YAML for each variant.

If the user is unsure which field is unique, ask before writing the view. A query view without a primary key will trigger a "Joins fan out the data without a primary key" error when joined. See: https://community.omni.co/t/why-am-i-getting-the-error-joins-fan-out-the-data-without-a-primary-key/37

Query views can also be defined inline within a topic's views: block, scoping the virtual table to that topic only. See references/topic-scoped-views.md for an example.

Common Validation Errors

Error	Fix
"No view X"	Check view name spelling
"No join path from X to Y"	Add a relationship
"Duplicate field name"	Remove duplicate or rename (or suppress with hidden: true if one is auto-generated)
"Invalid YAML syntax"	Check indentation (2 spaces, no tabs)
Fanout / incorrect aggregations on joins	Add primary_key: true to the joined view — every view that participates in a join must have a primary key
Column reference error (e.g., "Column X not found")	Check that the table exists and your Omni connection has access
Duplicate view appeared at the repo root after an edit	You wrote with a bare fileName instead of the file's full path. Delete the stray root file (send empty yaml to it) and re-write using the exact files key, including its folder prefix (e.g. MARTS/)

Troubleshooting: Model Out of Sync with Database

If the model doesn't reflect the database (missing columns/tables, wrong types, broken references), trigger a schema refresh (see "Schema Refresh" above), then omni models validate <modelId>. Field-name collisions and broken column references are usually fixed with hidden: true or a rename (see "Common Validation Errors"); persistent missing tables mean the connection lacks access to that database/schema.

Docs Reference

• Model YAML API · Views · Topics · Dimensions · Measures · Relationships · Query Views · Branch Mode

Related Skills

• omni-model-explorer — understand the model before modifying
• omni-ai-optimizer — add AI context after building topics
• omni-query — test new fields