Seeds for static reference data in dbt - Step-by-Step Execution

Concept Flow - Seeds for static reference data

Create CSV file with static data

↓

Place CSV in 'data' folder

↓

Define seed in dbt project

↓

Run 'dbt seed' command

↓

dbt loads CSV into database as table

↓

Use seed table in models/queries

This flow shows how you create a CSV file with static data, place it in your dbt project, run dbt seed to load it into your database, and then use it in your data models.

Execution Sample

dbt

id,name,category
1,Apple,Fruit
2,Carrot,Vegetable
3,Banana,Fruit

This CSV file contains static reference data for items with their categories.

Execution Table

Step	Action	Input/Command	Result
1	Create CSV file	id,name,category 1,Apple,Fruit 2,Carrot,Vegetable 3,Banana,Fruit	CSV file saved in 'data/items.csv'
2	Place CSV in data folder	Move 'items.csv' to 'data/'	'data/items.csv' available in project
3	Run dbt seed	dbt seed	CSV loaded into database as table 'items'
4	Use seed table in model	SELECT * FROM {{ ref('items') }}	Query returns static reference data from seed table
5	Modify CSV and rerun seed	Update CSV and run dbt seed	Database table 'items' updated with new data
6	Exit	No more actions	Static data ready for use in dbt models

💡 Static CSV data loaded into database as a seed table and ready for use.

Variable Tracker

Variable	Start	After Step 1	After Step 3	After Step 5	Final
CSV file	None	Created with 3 rows	Loaded into database as table 'items'	Updated with new data	Final seed table in database
Database table 'items'	None	None	Created with CSV data	Updated with new CSV data	Ready for queries

Key Moments - 3 Insights

Why do we need to place the CSV file in the 'data' folder?

dbt looks for seed files only in the 'data' folder, so placing the CSV there allows 'dbt seed' to find and load it, as shown in execution_table step 2.

What happens when we run 'dbt seed'?

The command reads all CSV files in the 'data' folder and loads them into the database as tables, replacing existing seed tables, as seen in execution_table step 3.

Can we update the seed data after initial load?

Yes, by modifying the CSV file and rerunning 'dbt seed', the database table updates with new data, shown in execution_table step 5.

Visual Quiz - 3 Questions

Look at the execution table, what is the result after running 'dbt seed' the first time?

A CSV file is created

B CSV file moved to 'data' folder

C CSV loaded into database as table 'items'

D Query returns static reference data

💡 Hint

Check execution_table row 3 under 'Result'

At which step does the static data become available for queries in dbt models?

A Step 3

B Step 4

C Step 2

D Step 5

💡 Hint

Look at execution_table where the seed table is referenced in a query

If you update the CSV file but forget to run 'dbt seed' again, what happens?

A Seed table remains unchanged in database

B Database table updates automatically

C dbt throws an error

D CSV file is deleted

💡 Hint

Refer to execution_table step 5 about updating CSV and rerunning seed

Concept Snapshot

Seeds in dbt are CSV files with static data.
Place CSVs in the 'data' folder.
Run 'dbt seed' to load CSVs as tables.
Use seed tables in models with {{ ref() }}.
Update CSV and rerun seed to refresh data.

Full Transcript

Seeds for static reference data in dbt involve creating CSV files with fixed data, placing them in the 'data' folder of your dbt project, and running the 'dbt seed' command. This command loads the CSV data into your database as tables. You can then use these seed tables in your dbt models by referencing them with the ref function. If you update the CSV files, rerunning 'dbt seed' updates the database tables accordingly. This process helps manage static data easily within your dbt workflows.