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My learningDBT Interview QuestionsLesson 14
Seeds in DBT: What They Are & When Not to Use Them

Lesson 14

Seeds in DBT: What They Are & When Not to Use Them

Seeds in DBT are small CSV files used to load static reference data like country codes or lookup tables into the warehouse. You should not use them for large, frequently changing, or transactional data because they are meant for small and stable datasets.

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Curriculum

20 lessons · 39m

0/20 lessons done39m left
  1. 0101:56

    What is DBT & Why Is It Important?

    01:56

  2. 0201:58

    Does dbt do the E, the L, or the T in ELT — and why does that distinction matter?

    01:58

  3. 0302:04

    DBT Core vs Platform vs Fusion Engine

    02:04

  4. 0402:01

    Why DBT Is an Analytics Engineering Tool

    02:01

  5. 0501:58

    Main Files & Folders in a DBT Project

    01:58

  6. 0601:54

    DBT Prerequisites Before Installation

    01:54

  7. 0701:55

    DBT Works With Your Data Warehouse, Not Instead of It

    01:55

  8. 0802:06

    DBT vs Traditional ETL Tools (Informatica & SSIS)

    02:06

  9. 0901:52

    When would you NOT use dbt?

    01:52

  10. 1001:58

    DBT vs Apache Airflow: Can They Work Together?

    01:58

  11. 1102:02

    Purpose of dbt_project.yml & Key Sections

    02:02

  12. 1201:54

    Sources vs Models in DBT

    01:54

  13. 1301:53

    Declaring and Referencing Sources in DBT

    01:53

  14. 01:42

    Seeds in DBT: What They Are & When Not to Use Them

    01:42

  15. 1501:54

    What is the difference between a seed and a source

    01:54

  16. 1602:24

    Source Freshness in DBT: Configuration & Testing

    02:24

  17. 1701:58

    Organizing 200+ Models in a Maintainable DBT Project

    01:58

  18. 1802:03

    Staging / Intermediate / Marts Folder Convention in DBT

    02:03

  19. 1901:55

    What is a dbt model, and what is its basic structure?

    01:55

  20. 2001:55

    What does the ref() function do, and why is it critical to dbt?

    01:55