Features

Everything you need to manage
reference data at scale

From editing dimension tables to syncing with your warehouse — DimTable is the single platform for your entire reference data lifecycle.

10+

Product features

50+

Pre-built datasets (rows)

Warehouse engines

Column types & metadata

Dimension Table Editor

A spreadsheet-grade editor, purpose-built for reference data

Create, edit, and manage dimension tables with a powerful inline editor. Filter, sort, group, freeze columns, import CSVs, and export to any format — all without leaving the browser.

Inline cell editing with type validation
Filter by any column (contains, equals, starts with, is empty)
Multi-column sorting (A→Z, Z→A)
Group rows by column with collapsible sections
Freeze first column for wide tables
Row numbers with bulk operations
CSV import with column mapping wizard
Export to CSV and SQL INSERT
Schema editor: rename, add, and delete columns on the fly
Table-level metadata: name, description, and owner fields

City	State	Population	Region
New York	NY	8,336,817	Northeast
Los Angeles	CA	3,979,576	West
Chicago	IL	2,693,976	Midwest

Template Marketplace

Pre-built dimensional models you can activate in one click

Browse curated collections of related dimension tables — like a complete Sales Quota Model with dims, bridges, and fact tables. Read about why each table exists, preview the schema and sample data, then activate what you need.

Curated collections with related tables
Per-table narratives explaining purpose and connections
Type badges: dimension, bridge, and fact tables
Column preview and sample data for every table
One-click activation into your workspace
Progress tracking: see which tables you've already activated
Tag-based search and filtering
New collections added regularly

DIMdim_territoryGeographic hierarchy

DIMdim_quota_seatNamed selling roles

BRIDGEbridge_seat_employeeWho sits where

FACTfact_quotaQuota amounts by period

Curated Datasets

Real-world reference data, ready to use

Pre-populated dimension tables with data you'd otherwise spend hours assembling. City demographics, geographic metadata, industry codes — sourced from Census, ACS, BLS, and NOAA. Activate into your workspace and start joining immediately.

Real data from authoritative public sources
US City Reference Data: 50 cities, 16 columns
Demographics: population, median income, median age
Geography: coordinates, metro areas, timezones, climate zones
Accessible via the same API as your custom tables
More datasets added regularly

City	Population	Income	Timezone
Austin, TX	961,855	$75,413	America/Chicago
Seattle, WA	737,015	$105,391	America/Los_Angeles
Denver, CO	715,522	$72,661	America/Denver

Warehouse Connections

Push and pull data between DimTable and your warehouse

Two-way sync with your data warehouse. Egress pushes your dimension tables into BigQuery as real, queryable tables. Ingress pulls source data in — like new locations from your POS — and upserts by key, preserving your manually-added metadata.

Egress: push all dimension tables to BigQuery
Ingress: pull data from warehouse queries into DimTable
Upsert by key: new rows added, existing rows updated
Manual column mapping (source → DimTable)
DimTable-only columns preserved during ingress sync
Preview source data before syncing
Sync history with status, duration, row counts
Scheduled syncs: manual, hourly, or daily
Encrypted credentials (AES-256-GCM)
BigQuery supported now, Snowflake + Postgres coming soon

Egress

DimTable → BigQuery

Push

Ingress

BigQuery → DimTable

Pull

AES-256-GCM encrypted credentials

Taxonomy Builder

Structured code generation for SKUs, campaigns, and more

Define taxonomy frameworks with categories, attributes, and validation rules — then generate perfectly structured codes. Think SKU systems, campaign naming conventions, account IDs. Each generated item can be pushed directly into a dimension table.

Define categories with nested attributes
Fixed-length validation per attribute
Lookup values with code → label mappings
Live preview as you build each code
Uniqueness enforcement across all generated items
Push items to any dimension table with field mapping
Both admin-provided and custom frameworks

taxonomy

Framework: Supplement SKU

Formulation
  Manufacturer  [2]  NG   → Nutrition Group
  Category      [2]  VS   → Vitamins & Supps
  Name          [4]  NITE → Nighttime
  Formula       [3]  1.0

Variant
  Sale Type     [3]  ONE  → One-Time Purchase
  Color         [3]  NA   → Not Associated
  Size          [3]  04W  → 4-Week Supply

→  NG-VS-NITE-1.0_ONE-NA-04W

Dynamic Code Templates

Production-ready SQL for every warehouse engine

SQL templates that generate dimension tables directly in your data warehouse. Written for BigQuery, Snowflake, and Redshift — each optimized for that engine's syntax, functions, and best practices. Copy, paste, run.

Engine-specific SQL (BigQuery, Snowflake, Redshift)
Copy-paste ready — no modifications needed
dbt-compatible model structure
Syntax highlighted with line numbers
One-click copy to clipboard
New templates added regularly

sql

-- dim_date (Snowflake)
WITH date_spine AS (
  SELECT DATEADD(day, seq, '2020-01-01')
    AS date_key
  FROM TABLE(GENERATOR(ROWCOUNT => 3650))
)
SELECT
  date_key,
  DAYOFWEEK(date_key) AS day_of_week,
  QUARTER(date_key)   AS fiscal_quarter
FROM date_spine

REST API

Pull dimension data into anything

A full REST API with key-based authentication, rate limiting, and three output formats. Pull your dimension tables into dbt, Airflow, Python scripts, or any tool that speaks HTTP.

JSON, CSV, and SQL INSERT output formats
Pagination for large tables
Column selection and filtering
API key management with prefix-based identification
Rate limiting (100 req/min per key)
Tenant-scoped — keys only access your org's data

bash

curl "https://dimtable.io/api/v1/tables
       /sales-region-mapping/rows
       ?format=json" \
  -H "Authorization: Bearer dt_live_..."

# → { "rows": [
#     { "country": "US",
#       "region": "West", ... }
#   ], "pagination": { ... } }

Developer Portal

Self-serve API key management and interactive docs

A built-in developer portal where your engineers can create API keys, browse endpoint documentation, and copy integration examples for Python, dbt, SQL, and cURL — all without leaving the product.

Create, list, and revoke API keys
Interactive endpoint documentation
Example code for Python, dbt, SQL, cURL
Request/response examples for every endpoint
Query parameter reference tables
Output format comparison (JSON, CSV, SQL)

GET/api/v1/tablesList all tables

GET/api/v1/tables/:slugGet schema

GET/api/v1/tables/:slug/rowsGet data

Team & Workspace

Role-based access, org settings, and account management

Invite your whole team with granular permissions. Manage workspace metadata, usage stats, and org-level settings. Every action is scoped to your organization — complete data isolation between customers.

Four roles: Owner, Admin, Editor, Viewer
Invite members by email
Workspace portal: Account ID, creation date, owner, usage stats
Org-level settings (toggle sidebar sections, etc.)
10-member limit per workspace (expandable)
Editable workspace name

Owner

Full control

Admin

Manage team + data

Editor

Edit rows

Viewer

Read only

Folders & Organization

Your file tree, your way

Create folders to organize dimension tables the way your team thinks about them — by department, domain, or project. Drag and drop tables between folders. Everything alphabetically sorted, always clean.

Create, rename, and delete folders
Drag-and-drop tables between folders
Alphabetical sorting by default
Unfoldered tables shown at root level
Collapsible folder tree in sidebar

Dimensions

▸ Finance

cost_center_mapping

gl_account_hierarchy

▸ Sales

territory_mapping

quota_seats

dim_city

See it all in action

Book a walkthrough and we'll show you every feature working with your data.

Book a Demo Contact Us

Everything you need to managereference data at scale