Yellow Capsule Medical

Dbt Testing, Documentation, and Packages

Anonymous published on 2026-02-13 included in Data Engineering

Goal: ensure data quality with Tests, generate beautiful Documentation, and leverage the community with Packages.

1. Testing: Sleep Well at Night

dbt makes testing a core part of the development workflow, not an afterthought.

Dbt Models: Subqueries, CTEs, and Macros

Anonymous published on 2026-02-12 included in Data Engineering

Goal: unlock the power of modular SQL with dbt Models, manage static data with Seeds, and write DRY code with Macros.

1. The Power of `{{ ref() }}`

In standard SQL, you reference tables by their name (FROM schema.table). In dbt, you use {{ ref('model_name') }}.

Dbt Project Setup: Structure, Sources, and Staging

Anonymous published on 2026-02-11 included in Data Engineering

Goal: master the anatomy of a dbt project, understand the staging layer, and learn how to define and use Sources.

1. Anatomy of a dbt Project

When you run dbt init, you get a standard directory structure. Here are the key components:

Dbt Architecture: Core, Cloud, and the Future With Fusion

Anonymous published on 2026-02-10 included in Data Engineering

Goal: Understand the architectural differences between dbt Core and dbt Cloud, and look ahead at the new dbt Fusion engine.

1. The Two Flavors of dbt

Historically, dbt has offered two distinct ways to run your transformations.

Analytics Engineering Basics & Introduction to Dbt

Anonymous published on 2026-02-09 included in Data Engineering

Goal: Understand the emergence of Analytics Engineering, the shift from ETL to ELT, and how dbt brings software engineering best practices to data transformation.

1. The Rise of Analytics Engineering

The data landscape has shifted dramatically in recent years, creating a gap that traditional roles (Data Engineer, Data Analyst, Data Scientist) struggled to fill.

Snowflake Architecture: Solving Pain Points With 'Separation of Storage and Compute'

Anonymous published on 2026-02-05 included in Data Engineering

Goal: Understand Snowflake’s Cloud-Native architecture, specifically the “Separation of Storage and Compute,” and how it solves traditional data warehousing pain points like concurrency and scaling.

1. The Architecture: A Hybrid Approach

Snowflake is not just a “Shared-Disk” or “Shared-Nothing” architecture; it is a Multi-Cluster, Shared Data architecture. It consists of three distinct layers: