Skip to main content

[Part#3] πŸ“Š Google SecOps Data in BigQuery: Demystifying Advanced BigQuery Export (SIEM) vs. Legacy BigQuery (SOAR)

  • September 1, 2026
  • 0 replies
  • 6 views

hzmndt
Staff
Forum|alt.badge.img+12

πŸ—„οΈ Part 3: SOAR BigQuery Schema Reference

Β 

(siemplify_search_everything_db)

TheΒ siemplify_search_everything_dbΒ dataset contains tables capturing the lifecycle of incident management, playbook automation, and analyst workflows.

Synchronization & Guardrails

  • Automated Sync:Β Handled continuously in the background by the SOAR data synchronization engine.
  • Simulated Cases Excluded:Β Cases created in simulation/test mode are deliberately excluded from BigQuery export to prevent corrupting production KPIs and SLA metrics.
  • Field Truncation:Β To adhere to BigQuery performance boundaries, field values exceedingΒ 10,000 charactersΒ are truncated.
  • Exact Table Naming:Β Keep in mind that some tables contain specific legacy naming conventions (e.g.,Β AlertProductsDistribuations). Use the exact literal table names in your SQL queries.

Key SOAR Tables Overview

Β 

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”

β”‚ DashboardCases β”‚

β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

β”‚

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”

β–Ό β–Ό β–Ό

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”

β”‚ DashboardAlertsβ”‚ β”‚ CaseTags β”‚ β”‚ CaseStageEntries β”‚

β””β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

β”‚

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”

β–Ό β–Ό β–Ό

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”

β”‚AlertEntityβ”‚β”‚Playbooks β”‚β”‚ActionRslt β”‚

β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

Table Category Key Tables Purpose / Data Captured
Cases & Lifecycle DashboardCases,Β CaseStageEntries,Β CaseAssignActivities,Β CaseMergeHistories Case status, priority, assigned analyst, stage transitions, closure reasons, merge records, and SLA status.
Alerts & Ingestion DashboardAlerts,Β AlertsDistribuations,Β AlertOntologyFamilies,Β AlertProductsDistribuations Source alerts grouped into cases, vendor products, alert rules, ontology families, and severity.
Playbooks & Actions DashboardAlertPlaybooks,Β WorkflowStepIndexRecords,Β SystemActionResults Automated playbook executions, individual action step runtimes, success/failure statuses, and script outputs.
Entities & Enrichments DashboardAlertEntities,Β InvolvedEntityRelations,Β AlertUsersDistribuations Extracted entities (IPs, hostnames, users), relations, whether entities are flagged as suspicious or internal.
SLA & Performance SystemCaseSlas,Β SystemAlertSlas,Β DashboardAlertCategoryOutcomes Target vs. actual SLA thresholds, resolution turnaround times, and alert outcome distributions.
Configuration Metadata MetadataCaseStages,Β MetadataSocRoles,Β MetadataUserProfiles,Β CustomFields,Β CustomFieldValues Definitions of case stages, team roles, analyst profiles, and custom case/closure field schemas.

πŸ’‘ High-Value SOAR BigQuery Use Cases & Sample Queries

Use Case 1: SOC Performance & MTTR by Case Root Cause

Measure how quickly cases are closed based on their root cause category.

Β 

sql

SELECT

RootCause,

COUNT(1) AS total_cases,

ROUND(AVG(TIMESTAMP_DIFF(ClosedTime, CreatedTime, MINUTE)), 2) AS avg_mttr_minutes,

ROUND(AVG(TIMESTAMP_DIFF(FirstAssignedTime, CreatedTime, MINUTE)), 2) AS avg_mtta_minutes

FROM

`YOUR_PROJECT_ID.siemplify_search_everything_db.DashboardCases`

WHERE

Status = 'Closed'

AND CreatedTime >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)

GROUP BY

RootCause

ORDER BY

total_cases DESC;

Use Case 2: Playbook Automation Health & Failure Rates

Identify playbooks or automated integration actions experiencing frequent failures or slow runtimes.

Β 

sql

SELECT

PlaybookName,

ActionName,

Status,

COUNT(1) AS execution_count,

ROUND(AVG(ExecutionDurationSeconds), 2) AS avg_duration_sec

FROM

`YOUR_PROJECT_ID.siemplify_search_everything_db.SystemActionResults`

WHERE

CreatedTime >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)

GROUP BY

PlaybookName,

ActionName,

Status

HAVING

Status IN ('Failed', 'Timeout', 'Error')

ORDER BY

execution_count DESC;

Use Case 3: Analyst Workload and Case Stage Progression

Track how cases move through stages and which analysts handle the highest volumes.

Β 

sql

SELECT

c.AssignedUser,

s.StageName,

COUNT(DISTINCT c.Id) AS active_cases,

ROUND(AVG(TIMESTAMP_DIFF(CURRENT_TIMESTAMP(), s.StartTime, HOUR)), 1) AS avg_hours_in_stage

FROM

`YOUR_PROJECT_ID.siemplify_search_everything_db.DashboardCases` c

JOIN

`YOUR_PROJECT_ID.siemplify_search_everything_db.CaseStageEntries` s

ON c.Id = s.CaseId

WHERE

c.Status = 'Open'

GROUP BY

c.AssignedUser,

s.StageName

ORDER BY

active_cases DESC;

βš–οΈ Summary Comparison: Advanced BQ Export vs. SOAR BQ

Feature Advanced BigQuery Export (SIEM) SOAR BigQuery (search_everything_db)
Primary Data Scope UDM Events, Rules/Detections, IoCs, Entity Graph Cases, Alerts, Playbooks, SOC SLAs, Users/Roles
Architecture Managed Tenant + Analytics Hub Linked Dataset Direct Managed / BYOBQ Dataset
Data Freshness Near real-time (< 5–10 mins) Synchronized continuous background sync
License Tier Enterprise Plus Requires SOAR Advanced Reporting
Dataset Name in BQ secops_linked_datalake siemplify_search_everything_db
Access Method Project IAM (roles/bigquery.dataViewer) Support Case or BackstoryΒ bigqueryAccessΒ API
Deduplication Automatic Fine-Grained DML (FGDML) Merges Managed backend synchronization

Helpful References