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The activities data table associated with your workspace logs record enrolments, node executions, and success/failure outcomes across all your tools and plays. These logs provide real-time visibility into how your workflows are performing—helping you identify bottlenecks, debug errors, and discover optimization opportunities.
You can query activities data from your data warehouse’s query editor, a Cargo data model, or the Sandbox query editor in the System of Record section of your workspace settings.
  • Adapt query syntax for your database (BigQuery or Snowflake) - If using your own data warehouse, the dataset name matches the Dataset name from your System of Record configuration - If using Cargo’s managed Snowflake, contact the Cargo team for connection details

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Understanding the activities table

The activities data is stored in the compute__activities table with the following structure:
ColumnDescription
_idUnique identifier for each activity
_timeTimestamp when the activity occurred
kindType of activity (see below)
model_uuidReference to the data model (if applicable)
workflow_uuidReference to the workflow
play_uuidReference to the play (if applicable)
tool_uuidReference to the tool (if applicable)
segment_uuidReference to the segment (for segment activities)
contextJSON object containing detailed activity information

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Activity types

KindDescription
workflowNodeExecutedA specific node in a workflow completed execution
workflowEnteredA record entered a workflow
workflowLeftA record exited a workflow
segmentEnteredA record entered a segment
segmentLeftA record left a segment

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Example: Track workflow node executions

Query all node executions for a specific workflow, including their status and any error messages: 
SELECT
  _time,
  JSON_VALUE(context, '$.nodeSlug') AS node_name,
  JSON_VALUE(context, '$.nodeKind') AS node_type,
  JSON_VALUE(context, '$.nodeStatus') AS status,
  JSON_VALUE(context, '$.nodeErrorMessage') AS error_message
FROM [DATASET].compute__activities
WHERE kind = 'workflowNodeExecuted'
  AND JSON_VALUE(context, '$.workflowUuid') = 'your-workflow-uuid-here'
ORDER BY _time DESC;

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Example: Track workflow throughput

Monitor how many records enter and exit your workflows over time: 
SELECT
  DATE(_time) AS date,
  kind,
  COUNT(*) AS count
FROM [DATASET].compute__activities
WHERE kind IN ('workflowEntered', 'workflowLeft')
  AND workflow_uuid = 'your-workflow-uuid-here'
GROUP BY date, kind
ORDER BY date DESC;

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Example: Identify failing nodes

Find nodes with the highest error rates: 
SELECT
  JSON_VALUE(context, '$.nodeSlug') AS node_name,
  COUNT(*) AS total_executions,
  COUNTIF(JSON_VALUE(context, '$.nodeStatus') = 'failed') AS failures,
  ROUND(
    COUNTIF(JSON_VALUE(context, '$.nodeStatus') = 'failed') * 100.0 / COUNT(*),
    2
  ) AS failure_rate
FROM [DATASET].compute__activities
WHERE kind = 'workflowNodeExecuted'
  AND _time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)
GROUP BY node_name
HAVING failures > 0
ORDER BY failure_rate DESC;

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Table naming conventions

Depending on your System of Record scope, use the appropriate table reference:
ScopeTable reference
BigQuery project-level[PROJECT_ID].[DATASET].compute__activities
BigQuery dataset-level[DATASET].compute__activities
Snowflake database-level[DATABASE]._compute.activities
Snowflake schema-level[DATABASE].[SCHEMA]._compute__activities
Replace bracketed values (e.g., [PROJECT_ID], [DATASET]) with your actual configuration values from the System of Record settings.