dbt

The dbt page provides full visibility into model-level cost and performance. Track how each job ran, identify which models consumed the most time or budget, and fine-tune performance by adjusting the Snowflake warehouse used for specific models and run types (Full Refresh or Incremental).

What you’ll see

• Execution Summary - a quick view of recent job runs with total execution time and cost. Spikes are easy to spot.

• Model Timeline (Gantt) - for any run, see what model ran when and how long it took.

• Model Drill-down - for a single model: last X runs with runtime and cost. From any row you can open the exact query in Snowflake (for deeper inspection).

Filters live in a top bar and apply across dbt while you browse (job selection, number of last runs, status, run type, etc.). Keep them simple: pick the job, choose how many runs to look at, and narrow if needed.

Optimize with model-level warehouse sizing

You can tune cost/performance per model, per job.

How to change a model’s size:

1. Open the model drill-down.

2. Choose Change warehouse size.

3. Select the target size.

4. Apply. The change is queued for the next job.

What you get back

• Average runtime and average cost.

• A Change History that records who changed what, when, and the impact (runtime/cost deltas after a few runs).

Changes Summary (job-level)

A single table to review and manage all model sizing for the selected job.

Use it to:

• Sort by Avg. runtime or Cost per run to find the best optimization targets.

• Bulk change several models at once.

Columns include model, run type, target size, change type (increase/decrease), number of runs used for impact stats, average runtime and cost deltas, materialization, and who changed it.

Optimization Workflows

The dbt page is designed for investigate → optimize → verify loops.

• Investigate a spike

  1. Spot the anomaly in Execution Summary.

  2. Open that day’s Timeline to see which model took longer.

  3. Drill down to Execution History for that model.

  4. Open the query in Snowflake if needed.

• Reduce recurring cost

  1. In Changes Summary, focus on Incremental models.

  2. Sort by Cost per run.

  3. Downsize the top offenders.

  4. Track runtime deltas in the Change History.

Why Use the dbt Page

• Transparency: Know exactly which models are consuming time and money.

• Granularity: Optimize at the model level, not just at the warehouse/job level.

• Accountability: Change History shows who made what adjustments and their effect.

• Control: Balance cost vs performance per model, per job, per run type.