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runQL creates and maintains clear descriptions across your schema so anyone can find the right data and meaning fast.

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Data Hub > Jaffle Shop > Dictionary > order_items
order_items
This table records each item that is part of a customer's order, detailing the product and its price. It helps in understanding what products are being sold and at what price, aiding in inventory and sales management.
Glossary:
Order Management Sales Analysis Product Details Product Pricing
Domains:
Retail E-commerce
order_id

varchar(255) • NOT NULL

This identifier connects an individual order item to the main customer order. It allows you to group all items that were purchased together in a single transaction. It is found in the order_id column of the order_items table and links to the id in the orders table.

Order Reference Order Tracking
References & Relationships
is_drink_item

tinyint(1) • NOT NULL

This is a True/False indicator that tells you whether the product is categorized as a drink item. It's useful for analyzing beverage sales, managing drink inventory, or understanding customer preferences for drinks. This is derived from the type column in the products table.

Identifier Order Tracking
References & Relationships
ordered_at

timestamp • NOT NULL

This field shows the exact date and time when a customer placed their order. It's useful for understanding sales trends over time, identifying peak ordering periods, and analyzing customer behavior patterns. It comes from the ordered_at column in the orders table.

Timestamp Sales Analysis
References & Relationships

What gets documented automatically

Not just table names, real descriptions people can actually use.

Table descriptions

What this table represents and how it should be used.

Column descriptions

Meaning, data types, formats, and example values.

Resource descriptions

The Sigma/Retool resources available and how they help.

Key business terms

Official definitions for metrics like "activation date" or "ARR".

Relationships

What connects to what, explained in plain language.

Related resources

Links to relevant dashboards, apps, and saved answers (queries).

How runQL generates and maintains descriptions

This is a one-time setup, then it keeps improving over time.

Connect Schema & Apps

Read-only access to schemas and external apps.

Add Context

Ingest internal docs, dbt docs, or our built-in Saleforce CRM and MS F&O docs.

Auto-Generate

Create metadata and business descriptions for all data and external resources.

Stay Current

New resources get auto documented; changes get flagged for review.

Edit anything to customize it as needed

You’re not stuck with the automated text. Your team owns the final definitions.

  • Edit any table or column description, glossary terms, or tags.
  • Human overrides - your specific wording automatically becomes the source of truth.
  • Add custom examples to show exactly what data values look like or automatically pull in ENUM values.
  • Add usage notes (e.g., “don’t use this for revenue reporting”).

is_drink_item

Rob D
By Rob D Updated Jan 02, 2026
Published
320 24
Example Question
What are the total number of drink orders in 2025?
Definition

This is a True/False indicator that tells you whether the product is categorized as a drink item. It's useful for analyzing beverage sales, managing drink inventory, or understanding customer preferences for drinks. This is derived from the type column in the products table.

Notes

0 means it is not a drink order, 1 means it is a drink order. Use this indicator to understand drink order history for example as part of a query to understand the revenue we make from drink orders or what type of drinks get ordered the most.

Tags
Identifier Order Tracking
Data Source Changes Detected
  • New column added to table:
    `orders`.`count_order_items`
    Description created
  • Column type changed:
    DECIMAL(10,2) -> INT in `orders`.`total_cents`
    Description updated
  • New table added:
    `customer_mapping`
    Description added

It stays current as your data changes

This is where manual documentation always breaks.

  • New tables & columns are automatically detected and documented instantly.
  • Schema changes surface as “needs review” (renames, type changes, meaning shifts).
  • Stop restarting documentation projects every quarter. It's always up to date.
  • Data Chat & Search keeps working because the underlying descriptions stay updated.

How much time this saves vs manual documentation

Documenting and maintiaing a real schema manually is a major project. runQL does the first pass automatically.

Manual Reality
  • A mid-size schema can have hundreds to thousands of columns.
  • Writing a usable definition isn’t just text—it’s meaning, caveats, usage notes.
  • Manual documentation typically becomes stale the moment migrations start.
With runQL
  • Generated descriptions across the whole schema in minutes.
  • Review focuses on the important 10–20% (IDs, key metrics, high-traffic tables).
  • Maintenance becomes incremental, not a re-write.
Schema Size
200 tables
×
Manual Time
3.5 min/col
=
One Time Total Effort
~400+ hours saved

Related features

Data Chat & Search

Ask questions in plain English and get pointed to the right place.

Automated Resource Dictionary

Index Sigma and Retool resources to prevent duplicate work.

One-Click Integrations

Connect your stack (database, Snowflake, Salesforce, Sigma, Slack) in minutes.

Make your data usable for the whole team.

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