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In this video we are covering dbt seeds. Dbt provides the seed function to import CSV data. By default dbt looks for csv files in the seeds directory. Using the seed command we can load CSV files to our data warehouse with a simple command. Dbt makes this very easy. The CSV files are located in our dbt repository, they are version controlled and code reviewable. Seeds are best suited to static data which changes infrequently. Seeds can be referenced in downstream models the same way as referencing models — by using the ref function. We load seeds data in this video and use it to create a dbt model. Link to dbt project (GitHub): https://github.com/hnawaz007/dbt-dw dbt seeds docs: https://docs.getdbt.com/docs/build/seeds Link to complete dbt series: https://hnawaz007.github.io/mds.html Load reference data using Python video: • How to load reference data to database wit... Link to Channel's site: https://hnawaz007.github.io/ -------------------------------------------------------------- 💥Subscribe to our channel: / haqnawaz 📌 Links ----------------------------------------- Follow me on social media! 🔗 GitHub: https://github.com/hnawaz007 📸 Instagram: / bi_insights_inc 📝 LinkedIn: / haq-nawaz 🔗 / hnawaz100 🚀 https://hnawaz007.github.io/ ----------------------------------------- #ETL #dbt #seeds Topics in this video (click to jump around): ================================== 0:00 - Introduction to data build tool (dbt) seeds 0:57 - DBT seeds use cases 1:13 - DBT project seeds configuration 1:39 - Add seeds data & data preview 2:31 - Run dbt seed command to load data 2:48 - Create dbt model using seeds data 3:36 - Test and review the new dbt model 3:50 - Review pipelines results