Seeds for static reference data in dbt - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a seed file in dbt.

dbt

version: 2
[1]:
  countries:
    path: data/countries.csv

Drag options to blanks, or click blank then click option'

Asnapshots

Btests

Cseeds

Dmodels

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'models' instead of 'seeds' in the schema file.

Confusing seeds with snapshots or tests.

The seeds key is used to define seed files in dbt's schema.yml.

2fill in blank

medium

Complete the code to load a seed file in dbt.

dbt

dbt [1] --select countries

Drag options to blanks, or click blank then click option'

Arun

Bseed

Ctest

Dsnapshot

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'dbt run' instead of 'dbt seed'.

Using 'dbt test' which runs tests, not seeds.

The dbt seed command loads CSV files defined as seeds into the data warehouse.

3fill in blank

hard

Fix the error in the seed configuration to disable header loading.

dbt

seeds:
  my_project:
    countries:
      [1]: false

Drag options to blanks, or click blank then click option'

Aheader

Bheaders

Cinclude_header

Dhas_header

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'headers' instead of 'header'.

Using keys like 'has_header' or 'include_header' which are invalid.

The correct config key to disable header loading in dbt seeds is header.

4fill in blank

hard

Fill all three blanks to create a dictionary comprehension filtering countries with names longer than 5 letters.

dbt

filtered = {country: data[country] [1] [2] len(country) [3] 5}

Drag options to blanks, or click blank then click option'

Afor country in data

B>

C<

Dif

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for filtering longer names.

Omitting 'for' or 'if' keywords.

The dictionary comprehension syntax is: {key: value for key in iterable if condition}.

5fill in blank

hard

Fill all three blanks to create a seed config that sets file path, header usage, and delimiter.

dbt

seeds:
  my_project:
    countries:
      [1]: data/countries.csv
      [2]: true
      [3]: ','

Drag options to blanks, or click blank then click option'

Apath

Bheader

Cdelimiter

Dformat

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'format' instead of 'delimiter'.

Confusing 'header' with 'headers'.

Seed config keys include path for file location, header to specify if the first row is header, and delimiter for the CSV separator.

Practice

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

easy

A. To create dynamic tables based on SQL queries

B. To load static reference data from CSV files into your database

C. To schedule dbt runs automatically

D. To write Python scripts for data transformation

Solution

1. Step 1: Understand what seeds are in dbt

   Seeds are CSV files that contain static reference data you want to load into your database.

2. Step 2: Identify the main use of seeds

   Seeds let you easily add fixed data tables without writing SQL queries.

3. Final Answer:

   To load static reference data from CSV files into your database -> Option B

4. Quick Check:

   Seeds = static CSV data load [OK]

Hint: Seeds = fixed CSV data loaded as tables [OK]

Common Mistakes:

• Confusing seeds with models that run SQL
• Thinking seeds schedule dbt runs
• Assuming seeds are for dynamic data

2. Which command do you run to load or refresh seed data in your database?

easy

A. dbt test

B. dbt run

C. dbt seed

D. dbt compile

Solution

1. Step 1: Recall dbt commands related to seeds

   The command dbt seed loads CSV seed files into the database as tables.

2. Step 2: Differentiate from other commands

   dbt run runs models, dbt test runs tests, and dbt compile compiles SQL but does not load seeds.

3. Final Answer:

   dbt seed -> Option C

4. Quick Check:

   Load seeds = dbt seed [OK]

Hint: Use 'dbt seed' to load CSV data tables [OK]

Common Mistakes:

• Using 'dbt run' to load seeds
• Confusing 'dbt test' with loading data
• Thinking 'dbt compile' loads data

3. Given a seed CSV file countries.csv with columns id and name, what will be the output of this dbt model SQL?

select * from {{ ref('countries') }}

medium

A. A table with all rows and columns from countries.csv

B. Only the id column from countries.csv

C. An empty table because seeds are not loaded automatically

D. An error because seeds cannot be referenced

Solution

1. Step 1: Understand how seeds are referenced in dbt

   Seeds become tables in the database and can be referenced using ref() like models.

2. Step 2: Predict the query output

   The query selects all columns and rows from the seed table countries, so it returns the full CSV data.

3. Final Answer:

   A table with all rows and columns from countries.csv -> Option A

4. Quick Check:

   ref(seed) = full seed table [OK]

Hint: ref(seed_name) returns full seed table [OK]

Common Mistakes:

• Thinking seeds cannot be referenced
• Assuming seeds load empty tables
• Expecting partial columns only

4. You ran dbt seed but your seed table did not update. Which of these is the most likely cause?

medium

A. You forgot to add the seed CSV file in the seeds folder

B. You ran dbt run instead of dbt seed

C. Your seed CSV file has syntax errors

D. You did not configure the seed in dbt_project.yml

Solution

1. Step 1: Check seed discovery mechanism

   dbt automatically discovers and loads CSV files from the seeds/ folder with dbt seed.

2. Step 2: Identify why table doesn't update

   If the CSV file is missing from the seeds/ folder, dbt seed runs successfully but skips that seed, leaving the table unchanged.

3. Final Answer:

   You forgot to add the seed CSV file in the seeds folder -> Option A

4. Quick Check:

   Seeds folder missing CSV = no update [OK]

Hint: Place seed CSVs in seeds/ folder for dbt seed [OK]

Common Mistakes:

• Thinking seeds require config in dbt_project.yml
• Confusing dbt run with dbt seed
• CSV syntax errors (would cause explicit failure)

5. You want to use a seed file currencies.csv with columns code and symbol inside a model to join with a transactions table on currency_code. Which is the correct way to write the join in your model SQL?

hard

A. select t.*, c.symbol from transactions t join currencies c on t.currency_code = c.code

B. select t.*, c.symbol from transactions t join currencies.csv c on t.currency_code = c.code

C. select t.*, c.symbol from transactions t join seed('currencies') c on t.currency_code = c.code

D. select t.*, c.symbol from transactions t join {{ ref('currencies') }} c on t.currency_code = c.code

Solution

1. Step 1: Recall how to reference seeds in dbt models

   Seeds are referenced using {{ ref('seed_name') }} to get the table name in SQL.

2. Step 2: Identify the correct join syntax

   Joining transactions with {{ ref('currencies') }} correctly uses the seed table in the join.

3. Final Answer:

   select t.*, c.symbol from transactions t join {{ ref('currencies') }} c on t.currency_code = c.code -> Option D

4. Quick Check:

   Join seed with ref() = correct [OK]

Hint: Use ref('seed_name') to join seed tables in models [OK]

Common Mistakes:

• Using raw CSV filename in SQL
• Forgetting to use ref() for seeds
• Trying to use a non-existent seed() function