Loading CSV seeds in dbt - Step-by-Step Execution

Concept Flow - Loading CSV seeds

Place CSV file in 'seeds/' folder

↓

Run 'dbt seed' command

↓

dbt reads CSV file

↓

dbt creates table in warehouse

↓

Table ready for use in models

This flow shows how dbt loads CSV seed files into your data warehouse as tables for use in your projects.

Execution Sample

dbt

dbt seed
-- loads CSV files from 'seeds/' folder into warehouse tables

This command reads CSV seed files and creates tables in your data warehouse.

Execution Table

Step	Action	Input	Output	Notes
1	Locate CSV file	'seeds/customers.csv'	File found	CSV file must be in 'seeds/' folder
2	Run command	dbt seed	Command starts	dbt begins processing seed files
3	Read CSV	'customers.csv'	CSV content loaded	dbt parses CSV rows and columns
4	Create table	CSV content	Table 'customers' created	Table schema inferred from CSV headers
5	Finish	Table created	Seed loading complete	Table ready for use in dbt models
6	Exit	All seeds processed	Process ends	No more CSV files to load

💡 All CSV seed files processed and tables created in warehouse

Variable Tracker

Variable	Start	After Step 3	After Step 4	Final
CSV file path	None	'seeds/customers.csv'	'seeds/customers.csv'	'seeds/customers.csv'
CSV content	None	Loaded rows and columns	Loaded rows and columns	Loaded rows and columns
Warehouse table	None	None	Table 'customers' created	Table 'customers' created

Key Moments - 3 Insights

Why must the CSV file be placed in the 'seeds/' folder?

dbt only looks for seed files inside the 'seeds/' folder when running 'dbt seed', so placing files elsewhere means they won't be loaded (see execution_table step 1).

How does dbt know the table schema from the CSV?

dbt reads the CSV header row to infer column names and types automatically during the read CSV step (execution_table step 3).

What happens if you run 'dbt seed' with no CSV files in 'seeds/'?

dbt runs but finds no files to load, so no tables are created and the process ends quickly (implied by exit_note).

Visual Quiz - 3 Questions

Look at the execution table, what is the output after step 4?

A Table 'customers' created

B CSV content loaded

C File found

D Seed loading complete

💡 Hint

Check the 'Output' column for step 4 in the execution_table.

At which step does dbt read the CSV file content?

A Step 4

B Step 2

C Step 3

D Step 5

💡 Hint

Look for the step where 'CSV content loaded' appears in the Output column.

If the CSV file is not in the 'seeds/' folder, what will happen when running 'dbt seed'?

A dbt will create an empty table

B dbt will not find the file and skip loading

C dbt will load the file anyway

D dbt will throw an error and stop

💡 Hint

Refer to key_moments about file location and execution_table step 1.

Concept Snapshot

Loading CSV seeds in dbt:
- Place CSV files in 'seeds/' folder
- Run 'dbt seed' command
- dbt reads CSV headers and data
- Creates tables in your warehouse
- Tables are ready for use in models

Full Transcript

Loading CSV seeds in dbt means putting your CSV files inside the 'seeds/' folder of your project. When you run the command 'dbt seed', dbt looks inside this folder, reads each CSV file, and creates a table in your data warehouse with the same name as the file. The table columns come from the CSV headers. This process lets you use static data easily in your dbt models. If the CSV file is not in the 'seeds/' folder, dbt will not find it and will skip loading it. After loading, the tables are ready to be queried or joined in your transformations.

Practice

1. What is the main purpose of loading CSV seeds in dbt?

easy

A. To load small, fixed reference data as tables in the database

B. To run complex SQL transformations on large datasets

C. To create temporary views for data exploration

D. To export data from the database to CSV files

Solution

1. Step 1: Understand the role of seeds in dbt

   Seeds are used to load CSV files as tables in the database, mainly for small, fixed reference data.

2. Step 2: Compare options with seed purpose

   Options B, C, and D describe other dbt or database functions, not seed loading.

3. Final Answer:

   To load small, fixed reference data as tables in the database -> Option A

4. Quick Check:

   Seeds = small fixed reference data [OK]

Hint: Seeds load small fixed data as tables [OK]

Common Mistakes:

• Thinking seeds are for large datasets
• Confusing seeds with models or views
• Assuming seeds export data instead of loading

2. Which folder should you place your CSV files in to load them as seeds in dbt?

easy

A. data

B. models

C. macros

D. snapshots

Solution

1. Step 1: Recall the folder structure for dbt seeds

   CSV files for seeds must be placed in the 'data' folder inside the dbt project.

2. Step 2: Eliminate other folders

   'models' is for SQL models, 'macros' for reusable code, 'snapshots' for snapshot data, so they are incorrect for seeds.

3. Final Answer:

   data -> Option A

4. Quick Check:

   Seeds folder = data [OK]

Hint: Put CSVs in 'data' folder for seeds [OK]

Common Mistakes:

• Placing CSVs in 'models' folder
• Confusing 'macros' with data storage
• Using 'snapshots' folder for seeds

3. Given the following dbt command run in a project with a CSV file named countries.csv in the data folder, what will happen?

dbt seed

medium

A. Nothing will happen unless you specify the table name

B. The CSV file will be deleted from the data folder

C. The CSV file will be loaded as a table named 'countries' in the database

D. The CSV file will be converted to a model SQL file

Solution

1. Step 1: Understand the effect of dbt seed

   This command loads all CSV files in the 'data' folder as tables in the database, using the CSV filename as the table name.

2. Step 2: Apply to the given CSV file

   The file 'countries.csv' will be loaded as a table named 'countries'. No extra arguments are needed.

3. Final Answer:

   The CSV file will be loaded as a table named 'countries' in the database -> Option C

4. Quick Check:

   dbt seed loads CSVs as tables named after files [OK]

Hint: dbt seed loads CSVs as tables named by file [OK]

Common Mistakes:

• Thinking dbt seed deletes files
• Expecting dbt seed to convert CSV to SQL
• Believing table name must be specified manually

4. You ran dbt seed but the table did not appear in your database. Which of the following is the most likely cause?

medium

A. The CSV file is empty

B. The CSV file has a .txt extension instead of .csv

C. You forgot to run dbt run after dbt seed

D. The CSV file is not placed inside the 'data' folder

Solution

1. Step 1: Check the seed loading requirements

   dbt only loads CSV files placed inside the 'data' folder when running dbt seed.

2. Step 2: Analyze the options

   If the CSV is outside 'data', dbt seed ignores it. A .txt file won't be loaded. Running dbt run is unrelated to seeds. An empty CSV still creates an empty table.

3. Final Answer:

   The CSV file is not placed inside the 'data' folder -> Option D

4. Quick Check:

   CSV must be in 'data' folder for seed [OK]

Hint: CSV must be in 'data' folder to load [OK]

Common Mistakes:

• Assuming dbt run loads seeds
• Ignoring file extension importance
• Thinking empty CSV prevents table creation

5. You have a CSV file products.csv with columns id, name, and price. You want to load it as a seed and then create a model that selects only products with price > 100. Which steps should you follow?

hard

A. Place products.csv in 'data', run dbt run, then create a model filtering price > 100

B. Place products.csv in 'data', run dbt seed, then create a model SQL selecting from the seed table filtering price > 100

C. Place products.csv in 'models', run dbt run, then filter price in the CSV file

D. Place products.csv in 'snapshots', run dbt seed, then create a model selecting all products

Solution

1. Step 1: Load CSV as seed

   Place the CSV in the 'data' folder and run dbt seed to load it as a table.

2. Step 2: Create a model filtering data

   Create a model SQL file that selects from the seed table and filters products where price > 100.

3. Step 3: Understand why other options fail

   Place products.csv in 'models', run dbt run, then filter price in the CSV file places CSV in wrong folder and filters CSV manually. Place products.csv in 'data', run dbt run, then create a model filtering price > 100 misses running dbt seed. Place products.csv in 'snapshots', run dbt seed, then create a model selecting all products uses wrong folder and does not filter.

4. Final Answer:

   Place products.csv in 'data', run dbt seed, then create a model SQL selecting from the seed table filtering price > 100 -> Option B

5. Quick Check:

   Seed CSV in 'data' + dbt seed + model filter = correct [OK]

Hint: Seed CSV in 'data', run dbt seed, then model filter [OK]

Common Mistakes:

• Placing CSV in wrong folder
• Skipping dbt seed command
• Filtering data outside SQL model