Write a query to identify the top three data pipelines by volume for each customer account over the last 30 days.
Use the run-level volume recorded in pipeline_runs and treat a NULL volume as zero. Include only pipelines with activity in the time period.
account_id, account_name, pipeline_id, pipeline_name, and total_volume_bytesaccount_id, then total volume descending, with pipeline_id ascending as the tie-breaker| Column | Type | Description |
|---|---|---|
| account_idPK | INT | Unique customer account identifier |
| account_name | VARCHAR(100) | Customer account name |
| Column | Type | Description |
|---|---|---|
| pipeline_idPK | INT | Unique data pipeline identifier |
| account_id | INT | Owning customer account |
| pipeline_name | VARCHAR(120) | Data pipeline name |
| Column | Type | Description |
|---|---|---|
| run_idPK | INT | Unique pipeline run identifier |
| pipeline_id | INT | Pipeline executed by the run |
| completed_at | TIMESTAMP | Timestamp when the run completed |
| volume_bytes | BIGINT | Bytes processed by the run |