Skip to main content

Execute DBT

ekai executes the generated DBT project directly in your connected data warehouse. Build models, run tests, and see results—all from the UI.

Prerequisites

Complete the AI Modeling & Build step to generate the DBT project artifacts.


DBT Project Tab

The DBT PROJECT tab in AI MODELING & BUILD shows:

  • File tree — Navigate project structure
  • Code view — SQL and YAML with syntax highlighting
  • Execution controls — Run and monitor builds
DBT Project execution

Running DBT

Start Execution

Click the Re-Run DBT Models button to execute:

Re-Run DBT Models

Execution Process

ekai runs dbt run --full-refresh in your warehouse:

  1. Compile — Generate SQL from models
  2. Execute — Run transformations in warehouse
  3. Test — Execute data validation tests
  4. Report — Show results and any errors

Execution Status

Success

EXECUTION COMPLETE — ALL DBT MODELS SUCCESSFULLY RUNNING

All models built and tests passed.

Summary Output

Summary

I successfully ran dbt run --full-refresh on your Snowflake-based
dbt project and fixed all errors encountered. Here's what was
accomplished:

Models Built:
✓ stg_temenos_customers
✓ stg_worldline_cards
✓ int_card_activation_enriched
✓ int_card_activation_metrics
✓ fct_card_activation
✓ dim_customers
✓ dim_card_type

Tests Passed: 12/12
Build Time: 45 seconds

AI Agent Chat During Execution

The AI Agent shows real-time progress:

Bash()

Running command: cat /app/workspaces/3/models/marts/
card_activation/fct_card_activation.sql | head -40

Write()

Writing to file: EXECUTION_GUIDE.md

Bash()

Running command: find . -type f ( -name "*.sql" -o
-name "*.yml" -o -name "*.md" ) ! -path "./dbt_packages/*" ! -pa...

Handling Errors

Common Error Types

ErrorCauseResolution
Compilation ErrorSQL syntax issueAI auto-fixes and retries
Test FailureData quality issueReview test results
Permission ErrorMissing warehouse accessCheck connection credentials
TimeoutLarge data volumeIncrease warehouse size

Auto-Fix Capability

ekai's AI Agent can automatically fix common errors:

"I encountered a compilation error in stg_customers.sql. The issue was a missing comma in the column list. I've fixed the error and re-running the build."


Incremental Execution

For subsequent runs:

ModeCommandWhen to Use
Full Refreshdbt run --full-refreshFirst run, schema changes
Incrementaldbt runRegular updates
Specific Modeldbt run -s model_nameTesting single model

Results Storage

After successful execution:

ArtifactLocation
Transformed TablesYour warehouse (configured schema)
Execution Logsekai logs directory
Test ResultsDBT test artifacts
DocumentationData catalog updated

Warehouse Requirements

Ensure your connected warehouse has:

RequirementDetails
Write AccessSchema where models will be created
ComputeSufficient warehouse size for transformations
StorageSpace for materialized tables
Read vs Write Access

Schema Agents only need read access. Semantic Models need write access to execute DBT and create tables.


Next Steps

  • Code Sync — Push to Git repository
  • Publish — Deploy to Snowflake Intelligence