dbt Core vs dbt Cloud - Visual Side-by-Side Comparison

Concept Flow - dbt Core vs dbt Cloud

Start: Choose dbt Tool

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Decision: Use Local or Cloud?

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Local

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dbt Core

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Manual Setup

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Run via CLI

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Output: Models

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End

This flow shows the choice between dbt Core (local, manual setup) and dbt Cloud (cloud, managed with UI and scheduler).

Execution Sample

dbt

1. Choose dbt Core or dbt Cloud
2. Write SQL models
3. Run models
4. Get transformed data

This simple sequence shows how both dbt Core and dbt Cloud run SQL models to transform data.

Execution Table

Step	Tool	Action	Environment	Output	Notes
1	dbt Core	Setup project locally	Local machine	Project files	Manual setup, requires CLI
2	dbt Core	Write SQL models	Local machine	SQL files	User writes models in code editor
3	dbt Core	Run models via CLI	Local machine	Transformed data	User runs commands manually
4	dbt Cloud	Setup project in cloud	Cloud platform	Project files	Managed setup with UI
5	dbt Cloud	Write SQL models	Cloud IDE	SQL files	Web-based editor
6	dbt Cloud	Run models via UI or scheduler	Cloud platform	Transformed data	Runs automatically or manually
7	dbt Cloud	Use collaboration features	Cloud platform	Team access	Version control and sharing
8	dbt Core	No built-in scheduler	Local machine	N/A	User must set up own scheduler
9	dbt Cloud	Built-in scheduler	Cloud platform	Automated runs	Runs scheduled jobs automatically
10	End	Compare outputs	N/A	Transformed data	Both produce same data outputs

💡 Both tools produce transformed data; choice depends on environment and features.

Variable Tracker

Variable	Start	After Step 2	After Step 3/6	Final
Project Setup	None	Local files (dbt Core) or Cloud project (dbt Cloud)	Same	Ready for runs
SQL Models	None	Written locally or in cloud IDE	Same	Used to transform data
Run Method	None	CLI (dbt Core) or UI/Scheduler (dbt Cloud)	Same	Triggers data transformation
Output Data	None	None	Transformed tables/views	Final transformed data

Key Moments - 3 Insights

Why does dbt Core require manual setup while dbt Cloud does not?

dbt Core runs locally and needs you to install and configure it yourself (see execution_table rows 1 and 4). dbt Cloud is a managed service with setup done through a web interface (row 4).

How do scheduling and automation differ between dbt Core and dbt Cloud?

dbt Core has no built-in scheduler, so you must set up your own (row 8). dbt Cloud includes a scheduler to run jobs automatically (row 9).

Do dbt Core and dbt Cloud produce different transformed data?

No, both produce the same transformed data from your SQL models (rows 3, 6, and 10). The difference is in how you run and manage the process.

Visual Quiz - 3 Questions

Look at the execution_table, at which step does dbt Cloud provide a web-based editor for SQL models?

A Step 2

B Step 5

C Step 3

D Step 7

💡 Hint

Check the 'Action' and 'Environment' columns for dbt Cloud steps in the execution_table.

According to variable_tracker, what is the state of 'Run Method' after step 3 for dbt Core?

A None

B UI/Scheduler

C CLI

D Automated

💡 Hint

Look at the 'Run Method' row and the 'After Step 3/6' column in variable_tracker.

If you want automatic job scheduling without manual setup, which tool and step should you focus on?

A dbt Cloud, Step 9

B dbt Core, Step 8

C dbt Core, Step 1

D dbt Cloud, Step 4

💡 Hint

Check execution_table rows about scheduling features for both tools.

Concept Snapshot

dbt Core runs locally with manual setup and CLI commands.
dbt Cloud is a managed service with web UI, scheduler, and collaboration.
Both use SQL models to transform data.
Choose Core for full control; Cloud for ease and automation.
Scheduling is built-in only in dbt Cloud.
Outputs are the same transformed data.

Full Transcript

This visual execution compares dbt Core and dbt Cloud. The flow starts with choosing between local or cloud environments. dbt Core requires manual setup on your computer and running commands via CLI. dbt Cloud offers a managed cloud platform with a web interface and scheduler. Both tools use SQL models to transform data, producing the same outputs. Key differences include setup complexity, scheduling, and collaboration features. The execution table traces each step for both tools, showing environment, actions, and outputs. Variable tracking shows how project setup, SQL models, run methods, and output data evolve. Key moments clarify common confusions about setup, scheduling, and output equivalence. The quiz tests understanding of steps and features. The snapshot summarizes the main points for quick recall.