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    Collect Metabase logs

    Supported in:

    This document explains how to ingest Metabase logs to Google Security Operations using Google Cloud Storage V2.

    Metabase is an open-source business intelligence and analytics platform that allows organizations to query databases, create visualizations, and build dashboards. On Pro and Enterprise plans, Metabase stores usage analytics and audit data in its application database, including activity logs, view logs, and query execution records. A Cloud Run function queries the Metabase API to retrieve this data and writes it to a GCS bucket for ingestion by Google SecOps.

    Before you begin

    Ensure that you have the following prerequisites:

    • A Google SecOps instance
    • A GCP project with Cloud Storage, Cloud Run, Pub/Sub, and Cloud Scheduler APIs enabled
    • Permissions to create and manage GCS buckets
    • Permissions to manage IAM policies on GCS buckets
    • Permissions to create Cloud Run services, Pub/Sub topics, and Cloud Scheduler jobs
    • Metabase Pro or Enterprise plan (usage analytics is not available on Open Source or Cloud Starter editions)
    • Metabase administrator access to create API keys
    • The Metabase application database added as a data source within Metabase (required for querying audit tables via API)

    Configure Metabase API access

    To enable Google SecOps to retrieve usage analytics data, you need to create an API key in Metabase and add the application database as a queryable data source.

    Create an API key

    1. Sign in to your Metabase instanceas an administrator.
    2. Click the gear iconin the upper right corner.
    3. Select Admin settings.
    4. Go to the Settingstab.
    5. Click on the Authenticationtab in the left menu.
    6. Scroll to API Keysand click Manage.
    7. Click the Create API Keybutton.
    8. Provide the following configuration details:
      • Key name: Enter a descriptive name (for example, Google SecOps Integration )
      • Group: Select a group with access to the Usage Analytics collection and query permissions for the application database
    9. Click Create.

    10. Copy and save the API key securely.

    Add the application database as a data source

    To query audit tables via the API, you must add the Metabase application database as a data source within Metabase.

    1. In Metabase, click the gear iconin the upper right corner.
    2. Select Admin settings.
    3. Go to the Databasestab.
    4. Click Add a database.
    5. Configure the database connection:
      • Database type: Select PostgreSQLor MySQL(depending on your application database)
      • Name: Enter Metabase Application Database
      • Host: Enter the hostname of your application database
      • Port: Enter the database port (default: 5432 for PostgreSQL, 3306 for MySQL)
      • Database name: Enter the application database name (for example, metabase )
      • Username: Enter a database user with SELECT permissions on the audit_log , view_log , and query_execution tables
      • Password: Enter the database password
    6. Click Save.
    7. After saving, note the Database IDfrom the URL (for example, https://metabase.example.com/admin/databases/5 means the database ID is 5 ).

    Verify permissions

    To verify the API key has the required permissions:

    1. Sign in to Metabase as an administrator.
    2. Click the gear icon > Admin settings > People.
    3. Find the group assigned to the API key.
    4. Verify the group has access to the Usage Analyticscollection.
    5. Go to Admin settings > Permissions > Data.
    6. Verify the group has Unrestrictedor Queryaccess to the application database.

    Test API access

    • Test your credentials before proceeding with the integration:

        # Replace with your actual values 
 METABASE_URL 
 = 
 "https://your-metabase-instance.com" 
 API_KEY 
 = 
 "mb_your_api_key_here" 
 DATABASE_ID 
 = 
 "5" 
 # Test API key authentication 
curl  
-s  
-H  
 "x-api-key: 
 ${ 
 API_KEY 
 } 
 " 
  
 \ 
  
 " 
 ${ 
 METABASE_URL 
 } 
 /api/database" 
  
 \ 
  
 | 
  
python3  
-m  
json.tool # Test querying the application database for audit_log records 
curl  
-s  
-X  
POST  
 " 
 ${ 
 METABASE_URL 
 } 
 /api/dataset/json" 
  
 \ 
  
-H  
 "x-api-key: 
 ${ 
 API_KEY 
 } 
 " 
  
 \ 
  
-H  
 "Content-Type: application/json" 
  
 \ 
  
-d  
 "{\"database\": 
 ${ 
 DATABASE_ID 
 } 
 , \"type\": \"native\", \"native\": {\"query\": \"SELECT * FROM audit_log ORDER BY id DESC LIMIT 5\", \"template-tags\": {}}, \"parameters\": []}" 
  
 \ 
  
 | 
  
python3  
-m  
json.tool

    A successful response returns a JSON array of recent audit log records.

    Create Google Cloud Storage bucket

    1. Go to the Google Cloud Console .
    2. Select your project or create a new one.
    3. In the navigation menu, go to Cloud Storage > Buckets.
    4. Click Create bucket.

    5. Provide the following configuration details:

      Setting Value
      Name your bucket Enter a globally unique name (for example, metabase-audit-logs )
      Location type Choose based on your needs (Region, Dual-region, Multi-region)
      Location Select the location (for example, us-central1 )
      Storage class Standard (recommended for frequently accessed logs)
      Access control Uniform (recommended)
      Protection tools Optional: Enable object versioning or retention policy

    6. Click Create.

    The Cloud Run function needs a service account with permissions to write to GCS bucket and be invoked by Pub/Sub.

    1. In the GCP Console, go to IAM & Admin > Service Accounts.
    2. Click Create Service Account.
    3. Provide the following configuration details:
      • Service account name: Enter metabase-audit-collector-sa
      • Service account description: Enter Service account for Cloud Run function to collect Metabase audit logs
    4. Click Create and Continue.
    5. In the Grant this service account access to projectsection, add the following roles:
      1. Click Select a role.
      2. Search for and select Storage Object Admin.
      3. Click + Add another role.
      4. Search for and select Cloud Run Invoker.
      5. Click + Add another role.
      6. Search for and select Cloud Functions Invoker.
    6. Click Continue.
    7. Click Done.

    These roles are required for:

    • Storage Object Admin: Write logs to GCS bucket and manage state files
    • Cloud Run Invoker: Allow Pub/Sub to invoke the function
    • Cloud Functions Invoker: Allow function invocation

    Grant IAM permissions on GCS bucket

    1. Go to Cloud Storage > Buckets.
    2. Click on your bucket name ( metabase-audit-logs ).
    3. Go to the Permissionstab.
    4. Click Grant access.
    5. Provide the following configuration details:
      • Add principals: Enter the service account email ( metabase-audit-collector-sa@PROJECT_ID.iam.gserviceaccount.com )
      • Assign roles: Select Storage Object Admin
    6. Click Save.

    Create Pub/Sub topic

    1. In the GCP Console, go to Pub/Sub > Topics.
    2. Click Create topic.
    3. Provide the following configuration details:
      • Topic ID: Enter metabase-audit-trigger
      • Leave other settings as default
    4. Click Create.

    Create Cloud Run function to collect logs

    The Cloud Run function will be triggered by Pub/Sub messages from Cloud Scheduler to fetch audit data from the Metabase API and write it to GCS.

    1. In the GCP Console, go to Cloud Run.
    2. Click Create service.
    3. Select Function(use an inline editor to create a function).

    4. In the Configuresection, provide the following configuration details:

      Setting Value
      Service name metabase-audit-collector
      Region Select region matching your GCS bucket (for example, us-central1 )
      Runtime Select Python 3.12or later

    5. In the Trigger (optional)section:

      1. Click + Add trigger.
      2. Select Cloud Pub/Sub.
      3. In Select a Cloud Pub/Sub topic, choose metabase-audit-trigger .
      4. Click Save.

    6. In the Authenticationsection:

      1. Select Require authentication.
      2. Check Identity and Access Management (IAM).

    7. Scroll down and expand Containers, Networking, Security.

    8. Go to the Securitytab:

      • Service account: Select metabase-audit-collector-sa

    9. Go to the Containerstab:

      1. Click Variables & Secrets.
      2. Click + Add variablefor each environment variable:
      Variable Name Example Value Description
      GCS_BUCKET
      		metabase-audit-logs GCS bucket name
      GCS_PREFIX
      		metabase-audit Prefix for log files
      STATE_KEY
      		metabase-audit/state.json State file path
      METABASE_URL
      		https://metabase.yourcompany.com Metabase instance base URL
      METABASE_API_KEY
      		mb_your_api_key_here Metabase API key
      DATABASE_ID
      		5 Application database ID
      LOOKBACK_HOURS
      		24 Initial lookback period
      PAGE_SIZE
      		2000 Records per API query

    10. In the Variables & Secretssection, scroll down to Requests:

      • Request timeout: Enter 600 seconds (10 minutes)

    11. Go to the Settingstab:

      • In the Resourcessection:
        • Memory: Select 512 MiBor higher
        • CPU: Select 1

    12. In the Revision scalingsection:

      • Minimum number of instances: Enter 0
      • Maximum number of instances: Enter 100

    13. Click Create.

    14. Wait for the service to be created (1-2 minutes).

    15. After the service is created, the inline code editorwill open automatically.

    Add function code

    1. Enter mainin the Entry pointfield.

    2. In the inline code editor, create two files:

      • main.py:

          import 
  
 functions_framework 
 from 
  
 google.cloud 
  
 import 
  storage 
 
 import 
  
 json 
 import 
  
 os 
 import 
  
 urllib3 
 from 
  
 datetime 
  
 import 
 datetime 
 , 
 timezone 
 , 
 timedelta 
 http 
 = 
 urllib3 
 . 
 PoolManager 
 ( 
 timeout 
 = 
 urllib3 
 . 
 Timeout 
 ( 
 connect 
 = 
 10.0 
 , 
 read 
 = 
 60.0 
 ), 
 retries 
 = 
 False 
 , 
 ) 
 storage_client 
 = 
  storage 
 
 . 
  Client 
 
 () 
 GCS_BUCKET 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'GCS_BUCKET' 
 ) 
 GCS_PREFIX 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'GCS_PREFIX' 
 , 
 'metabase-audit' 
 ) 
 STATE_KEY 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'STATE_KEY' 
 , 
 'metabase-audit/state.json' 
 ) 
 METABASE_URL 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'METABASE_URL' 
 , 
 '' 
 ) 
 . 
 rstrip 
 ( 
 '/' 
 ) 
 API_KEY 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'METABASE_API_KEY' 
 ) 
 DATABASE_ID 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'DATABASE_ID' 
 , 
 '1' 
 )) 
 LOOKBACK_HOURS 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'LOOKBACK_HOURS' 
 , 
 '24' 
 )) 
 PAGE_SIZE 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'PAGE_SIZE' 
 , 
 '2000' 
 )) 
 TABLES 
 = 
 [ 
 'audit_log' 
 , 
 'view_log' 
 , 
 'query_execution' 
 ] 
 TABLE_TIME_COLUMNS 
 = 
 { 
 'audit_log' 
 : 
 'timestamp' 
 , 
 'view_log' 
 : 
 'timestamp' 
 , 
 'query_execution' 
 : 
 'started_at' 
 , 
 } 
 @functions_framework 
 . 
 cloud_event 
 def 
  
 main 
 ( 
 cloud_event 
 ): 
 if 
 not 
 all 
 ([ 
 GCS_BUCKET 
 , 
 METABASE_URL 
 , 
 API_KEY 
 ]): 
 print 
 ( 
 'Error: Missing required environment variables' 
 ) 
 return 
 try 
 : 
 bucket 
 = 
 storage_client 
 . 
  bucket 
 
 ( 
 GCS_BUCKET 
 ) 
 state 
 = 
 load_state 
 ( 
 bucket 
 ) 
 now 
 = 
 datetime 
 . 
 now 
 ( 
 timezone 
 . 
 utc 
 ) 
 if 
 isinstance 
 ( 
 state 
 , 
 dict 
 ) 
 and 
  state 
 
 . 
 get 
 ( 
 'last_event_time' 
 ): 
 try 
 : 
 last_val 
 = 
 state 
 [ 
 'last_event_time' 
 ] 
 if 
 last_val 
 . 
 endswith 
 ( 
 'Z' 
 ): 
 last_val 
 = 
 last_val 
 [: 
 - 
 1 
 ] 
 + 
 '+00:00' 
 last_time 
 = 
 datetime 
 . 
 fromisoformat 
 ( 
 last_val 
 ) 
 last_time 
 = 
 last_time 
 - 
 timedelta 
 ( 
 minutes 
 = 
 2 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Warning: Could not parse last_event_time: 
 { 
 e 
 } 
 " 
 ) 
 last_time 
 = 
 now 
 - 
 timedelta 
 ( 
 hours 
 = 
 LOOKBACK_HOURS 
 ) 
 else 
 : 
 last_time 
 = 
 now 
 - 
 timedelta 
 ( 
 hours 
 = 
 LOOKBACK_HOURS 
 ) 
 print 
 ( 
 f 
 "Fetching logs from 
 { 
 last_time 
 . 
 isoformat 
 () 
 } 
 to 
 { 
 now 
 . 
 isoformat 
 () 
 } 
 " 
 ) 
 all_records 
 = 
 [] 
 newest_time 
 = 
 None 
 for 
 table 
 in 
 TABLES 
 : 
 time_col 
 = 
 TABLE_TIME_COLUMNS 
 [ 
 table 
 ] 
 records 
 = 
 fetch_table 
 ( 
 table 
 , 
 time_col 
 , 
 last_time 
 , 
 now 
 ) 
 for 
 r 
 in 
 records 
 : 
 r 
 [ 
 '_metabase_table' 
 ] 
 = 
 table 
 t 
 = 
 r 
 . 
 get 
 ( 
 time_col 
 ) 
 if 
 t 
 and 
 ( 
 newest_time 
 is 
 None 
 or 
 str 
 ( 
 t 
 ) 
> str 
 ( 
 newest_time 
 )): 
 newest_time 
 = 
 t 
 all_records 
 . 
 extend 
 ( 
 records 
 ) 
 print 
 ( 
 f 
 "Table 
 { 
 table 
 } 
 : fetched 
 { 
 len 
 ( 
 records 
 ) 
 } 
 records" 
 ) 
 if 
 not 
 all_records 
 : 
 print 
 ( 
 "No new records found." 
 ) 
 save_state 
 ( 
 bucket 
 , 
 now 
 . 
 isoformat 
 ()) 
 return 
 timestamp 
 = 
 now 
 . 
 strftime 
 ( 
 '%Y%m 
 %d 
 _%H%M%S' 
 ) 
 object_key 
 = 
 f 
 " 
 { 
 GCS_PREFIX 
 } 
 /metabase_audit_ 
 { 
 timestamp 
 } 
 .ndjson" 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 object_key 
 ) 
 ndjson 
 = 
 ' 
 \n 
 ' 
 . 
 join 
 ( 
 [ 
 json 
 . 
 dumps 
 ( 
 r 
 , 
 ensure_ascii 
 = 
 False 
 , 
 default 
 = 
 str 
 ) 
 for 
 r 
 in 
 all_records 
 ] 
 ) 
 + 
 ' 
 \n 
 ' 
 blob 
 . 
  upload_from_string 
 
 ( 
 ndjson 
 , 
 content_type 
 = 
 'application/x-ndjson' 
 ) 
 print 
 ( 
 f 
 "Wrote 
 { 
 len 
 ( 
 all_records 
 ) 
 } 
 records to gs:// 
 { 
 GCS_BUCKET 
 } 
 / 
 { 
 object_key 
 } 
 " 
 ) 
 save_state 
 ( 
 bucket 
 , 
 str 
 ( 
 newest_time 
 ) 
 if 
 newest_time 
 else 
 now 
 . 
 isoformat 
 ()) 
 print 
 ( 
 f 
 "Successfully processed 
 { 
 len 
 ( 
 all_records 
 ) 
 } 
 records" 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 'Error processing logs: 
 { 
 str 
 ( 
 e 
 ) 
 } 
 ' 
 ) 
 raise 
 def 
  
 fetch_table 
 ( 
 table 
 , 
 time_col 
 , 
 start_time 
 , 
 end_time 
 ): 
 start_str 
 = 
 start_time 
 . 
 strftime 
 ( 
 '%Y-%m- 
 %d 
 %H:%M:%S' 
 ) 
 end_str 
 = 
 end_time 
 . 
 strftime 
 ( 
 '%Y-%m- 
 %d 
 %H:%M:%S' 
 ) 
 all_records 
 = 
 [] 
 offset 
 = 
 0 
 max_pages 
 = 
 50 
 for 
 page 
 in 
 range 
 ( 
 max_pages 
 ): 
 sql 
 = 
 ( 
 f 
 "SELECT * FROM 
 { 
 table 
 } 
 " 
 f 
 "WHERE 
 { 
 time_col 
 } 
 >= ' 
 { 
 start_str 
 } 
 ' " 
 f 
 "AND 
 { 
 time_col 
 } 
 < ' 
 { 
 end_str 
 } 
 ' " 
 f 
 "ORDER BY 
 { 
 time_col 
 } 
 ASC " 
 f 
 "LIMIT 
 { 
 PAGE_SIZE 
 } 
 OFFSET 
 { 
 offset 
 } 
 " 
 ) 
 query_body 
 = 
 { 
 "database" 
 : 
 DATABASE_ID 
 , 
 "type" 
 : 
 "native" 
 , 
 "native" 
 : 
 { 
 "query" 
 : 
 sql 
 , 
 "template-tags" 
 : 
 {} 
 }, 
 "parameters" 
 : 
 [] 
 } 
 url 
 = 
 f 
 " 
 { 
 METABASE_URL 
 } 
 /api/dataset/json" 
 response 
 = 
 http 
 . 
 request 
 ( 
 'POST' 
 , 
 url 
 , 
 body 
 = 
 json 
 . 
 dumps 
 ( 
 query_body 
 ) 
 . 
 encode 
 ( 
 'utf-8' 
 ), 
 headers 
 = 
 { 
 'x-api-key' 
 : 
 API_KEY 
 , 
 'Content-Type' 
 : 
 'application/json' 
 } 
 ) 
 if 
 response 
 . 
 status 
 == 
 429 
 : 
 print 
 ( 
 f 
 "Rate limited on 
 { 
 table 
 } 
 query. Stopping pagination." 
 ) 
 break 
 if 
 response 
 . 
 status 
 != 
 200 
 : 
 print 
 ( 
 f 
 " 
 { 
 table 
 } 
 query failed: 
 { 
 response 
 . 
 status 
 } 
 - " 
 f 
 " 
 { 
 response 
 . 
 data 
 . 
 decode 
 ( 
 'utf-8' 
 ) 
 } 
 " 
 ) 
 break 
 page_results 
 = 
 json 
 . 
 loads 
 ( 
 response 
 . 
 data 
 . 
 decode 
 ( 
 'utf-8' 
 )) 
 if 
 not 
 page_results 
 : 
 break 
 all_records 
 . 
 extend 
 ( 
 page_results 
 ) 
 print 
 ( 
 f 
 " 
 { 
 table 
 } 
 page 
 { 
 page 
  
 + 
  
 1 
 } 
 : 
 { 
 len 
 ( 
 page_results 
 ) 
 } 
 records " 
 f 
 "(total: 
 { 
 len 
 ( 
 all_records 
 ) 
 } 
 )" 
 ) 
 if 
 len 
 ( 
 page_results 
 ) 
< PAGE_SIZE 
 : 
 break 
 offset 
 += 
 PAGE_SIZE 
 return 
 all_records 
 def 
  
 load_state 
 ( 
 bucket 
 ): 
 try 
 : 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 STATE_KEY 
 ) 
 if 
 blob 
 . 
 exists 
 (): 
 return 
 json 
 . 
 loads 
 ( 
 blob 
 . 
  download_as_text 
 
 ()) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Warning: Could not load state: 
 { 
 e 
 } 
 " 
 ) 
 return 
 {} 
 def 
  
 save_state 
 ( 
 bucket 
 , 
 last_event_time_iso 
 ): 
 try 
 : 
 state 
 = 
 { 
 'last_event_time' 
 : 
 last_event_time_iso 
 , 
 'last_run' 
 : 
 datetime 
 . 
 now 
 ( 
 timezone 
 . 
 utc 
 ) 
 . 
 isoformat 
 () 
 } 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 STATE_KEY 
 ) 
 blob 
 . 
  upload_from_string 
 
 ( 
 json 
 . 
 dumps 
 ( 
 state 
 , 
 indent 
 = 
 2 
 ), 
 content_type 
 = 
 'application/json' 
 ) 
 print 
 ( 
 f 
 "Saved state: last_event_time= 
 { 
 last_event_time_iso 
 } 
 " 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Warning: Could not save state: 
 { 
 e 
 } 
 " 
 )

      • requirements.txt:

       functions-framework==3.*
google-cloud-storage==2.*
urllib3>=2.0.0

    3. Click Deployto save and deploy the function.

    4. Wait for deployment to complete (2-3 minutes).

    Create Cloud Scheduler job

    1. In the GCP Console, go to Cloud Scheduler.
    2. Click Create Job.

    3. Provide the following configuration details:

      Setting Value
      Name metabase-audit-collector-hourly
      Region Select same region as Cloud Run function
      Frequency 0 * * * * (every hour, on the hour)
      Timezone Select timezone (UTC recommended)
      Target type Pub/Sub
      Topic Select metabase-audit-trigger
      Message body {} (empty JSON object)

    4. Click Create.

    Schedule frequency options

    Choose frequency based on log volume and latency requirements:

    Frequency Cron Expression Use Case
    Every 5 minutes
    		 */5 * * * * High-volume, low-latency
    Every 15 minutes
    		 */15 * * * * Medium volume
    Every hour
    		 0 * * * * Standard (recommended)
    Every 6 hours
    		 0 */6 * * * Low volume, batch processing
    Daily
    		 0 0 * * * Historical data collection

    Test the integration

    1. In the Cloud Schedulerconsole, find your job ( metabase-audit-collector-hourly ).
    2. Click Force runto trigger the job manually.
    3. Wait a few seconds.
    4. Go to Cloud Run > Services.
    5. Click on metabase-audit-collector .
    6. Click the Logstab.

    7. Verify the function executed successfully. Look for:

       Fetching logs from YYYY-MM-DDTHH:MM:SS+00:00 to YYYY-MM-DDTHH:MM:SS+00:00
Table audit_log: fetched X records
Table view_log: fetched X records
Table query_execution: fetched X records
Wrote X records to gs://metabase-audit-logs/metabase-audit/metabase_audit_YYYYMMDD_HHMMSS.ndjson
Successfully processed X records

    8. Go to Cloud Storage > Buckets.

    9. Click on metabase-audit-logs .

    10. Navigate to the metabase-audit/ folder.

    11. Verify that a new .ndjson file was created with the current timestamp.

    If you see errors in the logs:

    • HTTP 401: Verify the METABASE_API_KEY environment variable is correct and the key has not been deleted or regenerated
    • HTTP 403: Verify the API key's group has query permissions for the application database
    • HTTP 429: Rate limiting -- the function will stop pagination and resume on the next scheduled run
    • Missing environment variables: Verify all required variables are set in the Cloud Run function configuration
    • Empty results: Verify the DATABASE_ID is correct, the application database is added as a data source, and you are using Metabase Pro or Enterprise
    1. Go to SIEM Settings > Feeds.
    2. Click Add New Feed.
    3. Click Configure a single feed.
    4. In the Feed namefield, enter a name for the feed (for example, Metabase Audit Logs ).
    5. Select Google Cloud Storage V2as the Source type.
    6. Select METABASEas the Log type.

    7. Click Get Service Account. A unique service account email will be displayed, for example:

       chronicle-12345678@chronicle-gcp-prod.iam.gserviceaccount.com

    8. Copy this email address for use in the next step.

    9. Click Next.

    10. Specify values for the following input parameters:

      • Storage bucket URL: Enter the GCS bucket URI with the prefix path:

         gs://metabase-audit-logs/metabase-audit/

      • Source deletion option: Select the deletion option according to your preference:

        • Never: Never deletes any files after transfers (recommended for testing).
        • Delete transferred files: Deletes files after successful transfer.

        • Delete transferred files and empty directories: Deletes files and empty directories after successful transfer.

      • Maximum File Age: Include files modified in the last number of days (default is 180 days)

      • Asset namespace: The asset namespace

      • Ingestion labels: The label to be applied to the events from this feed

    11. Click Next.

    12. Review your new feed configuration in the Finalizescreen, and then click Submit.

    The Google SecOps service account needs Storage Object Viewerrole on your GCS bucket.

    1. Go to Cloud Storage > Buckets.
    2. Click on metabase-audit-logs .
    3. Go to the Permissionstab.
    4. Click Grant access.
    5. Provide the following configuration details:
      • Add principals: Paste the Google SecOps service account email
      • Assign roles: Select Storage Object Viewer

    6. Click Save.

    Usage analytics tables

    The Cloud Run function queries the following tables from the Metabase application database:

    Table Name Description
    audit_log Records of configuration changes, user actions, and system events
    view_log Tracks views of cards (questions/models), dashboards, and tables
    query_execution Information about all queries executed across all dashboards

    Data retention

    By default, Metabase retains usage analytics data for 720 days (approximately 2 years). Twice daily, Metabase automatically deletes rows older than this threshold. To change the retention period, set the environment variable MB_AUDIT_MAX_RETENTION_DAYS when running Metabase.

    UDM mapping table

    Log Field UDM Mapping Logic
    additional
    		 additional Renamed from additional
    locale
    		 additional.fields Merged with respective labels (e.g., locale_label for locale, is_active_label for is_active, etc.)
    is_active
    		 additional.fields
    is_qbnewb
    		 additional.fields
    group_ids
    		 additional.fields
    is_superuser
    		 additional.fields
    login_attributes
    		 additional.fields
    id
    		 additional.fields
    sso_source
    		 additional.fields
    personal_collection_id
    		 additional.fields
    metadata
    		 metadata Renamed from metadata
    updated_at
    		 metadata.event_timestamp Date parsed from updated_at using ISO8601, RFC3339, or yyyy-MM-ddTHH:mm:ss.SSSSSSZ
    has_userid
    		 metadata.event_type Set to "USER_UNCATEGORIZED" if has_userid == "true", else "GENERIC_EVENT"
    principal
    		 principal Renamed from principal
    metabase_host
    		 principal.url Value copied directly
    date_joined
    		 principal.user.attribute.creation_time Date parsed from date_joined using ISO8601, RFC3339, or yyyy-MM-ddTHH:mm:ss.SSSSSSZ
    email
    		 principal.user.email_addresses Merged from email
    first_name
    		 principal.user.first_name Value copied directly
    last_login
    		 principal.user.last_login_time Date parsed from last_login using ISO8601, RFC3339, or yyyy-MM-ddTHH:mm:ss.SSSSSSZ
    last_name
    		 principal.user.last_name Value copied directly
    common_name
    		 principal.user.user_display_name Value copied directly
    email
    		 principal.user.userid Value copied directly from email
    security_result
    		 security_result Merged from security_result
    target
    		 target Renamed from target
    metadata.product_name
    		 metadata.product_name Set to "METABASE"
    metadata.vendor_name
    		 metadata.vendor_name Set to "METABASE"

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