Runway allows you to write SQL directly within the app to pull, shape, and transform data based on your needs. This feature is available for integrations using one of our supported data warehouses or any of our Fivetran integrations.
This guide is designed for users with SQL experience or for data teams assisting business users in integrating data into Runway. If you’re new to SQL, consider learning through resources like Snowflake’s SQL introduction.
For integrations that support SQL queries, you can click + next to integration name in the sidebar to create a new query.You can then change the default query name, New query, to something more descriptive. To do this, click the three-dots button next to the query name in the sidebar and select Rename query.
When querying data warehouses (non-Fivetran integrations), you must use the SQL syntax of the provider:
Google BigQuery – Uses GoogleSQL
Snowflake – Supports standard SQL
Amazon Redshift – Uses PostgreSQL
Runway automatically runs your queries every morning (US Pacific Time), updating any dependent databases, models, and pages. Queries run directly against your data warehouse, so results match what you’d get if you queried locally in that system.You can manually refresh data at any time by going into the query and selecting Run query.
You cannot write SQL to join data warehouse sources with Fivetran integrations within Runway.
All Fivetran integrations are stored in Runway’s Snowflake data warehouse, meaning all SQL queries must follow Snowflake-supported syntax.To explore available data, open the query editor in Runway for a Fivetran integration and run:
SHOW TABLES
This command will list all schemas and tables available for querying. If you have multipleFivetran integrations, you will see matching tables and schemas across different integrations, allowing you to join multiple Fivetran sources together.Runway automatically syncs data daily:
Data is pulled from your source system into Runway’s Snowflake data warehouse every morning (US Pacific Time).
Runway then automaticallyexecutes integration queries shortly after to refresh the data in your model.
If you need an immediate manual refresh:
Manually trigger a sync from your source system.
Once the source data is refreshed, rerun your SQL queries manually to update your model. This step ensures the newly synced data appears in your model.
Structure your queries to return a long dataset—a list of transactions, accounts, or metrics with a daily or monthly amount. This format ensures flexibility for modeling. Example:
dimension_1
dimension_..x
date_1
date_..x
value_1
value_..x
Account 1
Asset
01/01/2025
03/01/2025
10
20
Account 2
Liability
01/02/2025
04/01/2025
30
40
Include as all dimensions, dates, or values needed—not all of them need to be used in the final database configuration.
Aggregate/roll up data to the minimum level required for modeling and drill-downs. For example, if pulling general ledger data, and only monthly spend per account is needed, write your query to pre-aggregate the data:
Avoid this
select account, transaction_amount, transaction_datefrom general ledger
Do this
select account, sum(transaction_amount) as transaction_amount, date_trunc('month',transaction_date) transaction_monthfrom general ledgergroup by 1,3
Filter out unwanted data—some Fivetran tables contain columns like _fivetran_deleted. Filtering out these records helps prevent duplicates records in your dataset.
