Dim and fact models in dbt | Codebasics

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Contents

1.1: Why dbt? The problem with plain SQL scripts
1.2: How dbt works

2.1: Snowflake Setup and Loading Data
2.2: Prepare Snowflake for dbt
2.3: Install_dbt_Core
2.4: Create a Project and Connect to Snowflake

3.1: Sources and your First Model
3.2: ref() and the DAG
3.3: Materializations- View, Table, Ephemeral
3.4: Incremental Load
3.5: Layering and Selection

4.1: Why test? Generic tests
4.2: Singular tests, Severity and Store Failures
4.3: Packages and Source Freshness

5.1: Jinja Basics Inside SQL
5.2: Macros: Write your own and Use dbt_utils
5.3: Variables and Hooks

6.1: Seeds
6.2: Snapshots: SCD Type 2
6.3: Dim and fact models in dbt

7.1: Documentation and Lineage
7.2: Dev vs Prod, git workflow
7.3: CI/CD with GitHub Actions
7.4: Run dbt with Airflow

Instructor

Hemanand

Hemanand Vadivel

Ex- Data Analytics Manager, 8+ Years in Europe, Microsoft Certified, Certified Supply Chain Professional

I’m a mechanical engineer who transitioned to a full time Data & Analytics manager in the UK & Germany by teaching myself Power BI, excel & anything else that was required to solve the problem. I have worked with complex data across Supply Chain, Sales, Marketing, Revenue Management, Finance and HR functions over the last 8 years to deliver effective solutions. To me, Analytics is an extra sensory organ that anyone can develop to become a superhuman at work. I have conducted several workshops and internal training to help non-technical people to become superhuman at work and now, really excited for this opportunity to share all that knowledge with you in this course.

Dim and fact models in dbt

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