S3 & BigQuery Archival

For compliance frameworks like SOC 2, HIPAA, and GDPR, organizations need to retain authorization events for extended periods. The @grantex/destinations package provides S3Destination and BigQueryDestination classes that archive events to durable storage for long-term retention and analytics.

Prerequisites

The @grantex/destinations package installed:

npm install @grantex/destinations

For S3: AWS credentials configured (environment variables, IAM role, or ~/.aws/credentials)
For BigQuery: Google Cloud credentials configured (service account key or Application Default Credentials)

Amazon S3

Setup

import { EventSource, S3Destination } from '@grantex/destinations';

const source = new EventSource({
  url: 'https://api.grantex.dev',
  apiKey: process.env.GRANTEX_API_KEY!,
});

const s3 = new S3Destination({
  bucket: 'my-company-grantex-events',
  prefix: 'grantex-events',
  region: 'us-east-1',
  batchSize: 1000,
  flushIntervalMs: 60000,  // flush every 60 seconds
});

source.addDestination(s3);
await source.start();

Configuration Options

Option	Type	Default	Description
`bucket`	`string`	required	S3 bucket name
`prefix`	`string`	`grantex-events`	Key prefix for uploaded objects
`region`	`string`	`us-east-1`	AWS region
`batchSize`	`number`	`1000`	Number of events to buffer before flushing
`flushIntervalMs`	`number`	—	Flush buffered events on a timer (milliseconds)

How It Works

The S3Destination buffers events and writes them as NDJSON (newline-delimited JSON) files to S3. Each flush produces one object with a timestamped key:

s3://my-company-grantex-events/grantex-events/2026-03-01T12-00-00-000Z.ndjson

Each line in the file is a complete JSON event:

{"id":"evt_01...","type":"grant.created","createdAt":"2026-03-01T12:00:00Z","data":{"grantId":"grnt_01...","agentId":"ag_01..."}}
{"id":"evt_02...","type":"token.issued","createdAt":"2026-03-01T12:00:01Z","data":{"tokenId":"tok_01...","grantId":"grnt_01..."}}

The S3 destination dynamically imports @aws-sdk/client-s3 at runtime. Install it as a peer dependency: npm install @aws-sdk/client-s3.

IAM Policy

The S3 destination requires s3:PutObject permission. Attach this policy to your IAM role or user:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "s3:PutObject",
      "Resource": "arn:aws:s3:::my-company-grantex-events/grantex-events/*"
    }
  ]
}

S3 Lifecycle Policy

Configure an S3 lifecycle policy for cost-effective long-term retention:

{
  "Rules": [
    {
      "ID": "grantex-events-archival",
      "Filter": { "Prefix": "grantex-events/" },
      "Status": "Enabled",
      "Transitions": [
        { "Days": 30, "StorageClass": "STANDARD_IA" },
        { "Days": 90, "StorageClass": "GLACIER" },
        { "Days": 365, "StorageClass": "DEEP_ARCHIVE" }
      ],
      "Expiration": { "Days": 2555 }
    }
  ]
}

This policy:

Moves objects to Standard-IA after 30 days
Moves to Glacier after 90 days
Moves to Deep Archive after 1 year
Deletes after 7 years (adjust per your retention requirements)

Querying with Athena

Set up an Athena table to query your archived events with standard SQL:

CREATE EXTERNAL TABLE grantex_events (
  id        STRING,
  type      STRING,
  createdAt STRING,
  data      STRING
)
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe'
LOCATION 's3://my-company-grantex-events/grantex-events/'
TBLPROPERTIES ('has_encrypted_data'='false');

Example queries:

-- Count events by type in the last 7 days
SELECT type, COUNT(*) as cnt
FROM grantex_events
WHERE createdAt >= date_format(date_add('day', -7, current_timestamp), '%Y-%m-%dT%H:%i:%sZ')
GROUP BY type
ORDER BY cnt DESC;

-- Find all revocations for a specific principal
SELECT *
FROM grantex_events
WHERE type = 'grant.revoked'
  AND json_extract_scalar(data, '$.principalId') = 'user-123';

Google BigQuery

Setup

import { EventSource, BigQueryDestination } from '@grantex/destinations';

const source = new EventSource({
  url: 'https://api.grantex.dev',
  apiKey: process.env.GRANTEX_API_KEY!,
});

const bigquery = new BigQueryDestination({
  projectId: 'my-gcp-project',
  datasetId: 'grantex',
  tableId: 'events',
  batchSize: 500,
  flushIntervalMs: 30000,  // flush every 30 seconds
});

source.addDestination(bigquery);
await source.start();

Configuration Options

Option	Type	Default	Description
`projectId`	`string`	required	Google Cloud project ID
`datasetId`	`string`	required	BigQuery dataset ID
`tableId`	`string`	required	BigQuery table ID
`batchSize`	`number`	`500`	Number of events to buffer before flushing
`flushIntervalMs`	`number`	—	Flush buffered events on a timer (milliseconds)

How It Works

The BigQueryDestination buffers events and inserts them as rows into a BigQuery table using the streaming insert API. Each event maps to a row with these columns:

Column	BigQuery Type	Source
`event_id`	`STRING`	`event.id`
`event_type`	`STRING`	`event.type`
`created_at`	`STRING`	`event.createdAt`
`data`	`STRING`	`JSON.stringify(event.data)`

The BigQuery destination dynamically imports @google-cloud/bigquery at runtime. Install it as a peer dependency: npm install @google-cloud/bigquery.

Table Schema

Create the BigQuery table before starting the destination:

CREATE TABLE `my-gcp-project.grantex.events` (
  event_id   STRING NOT NULL,
  event_type STRING NOT NULL,
  created_at STRING NOT NULL,
  data       STRING
)
PARTITION BY DATE(PARSE_TIMESTAMP('%Y-%m-%dT%H:%M:%SZ', created_at))
CLUSTER BY event_type;

Partitioning by date and clustering by event_type gives you fast queries and lower costs for time-range and type-filtered queries.

IAM Permissions

The service account needs these BigQuery permissions:

bigquery.tables.updateData (for streaming inserts)
bigquery.tables.get (to verify table existence)

Grant the BigQuery Data Editor role on the dataset:

gcloud projects add-iam-policy-binding my-gcp-project \
  --member="serviceAccount:grantex-events@my-gcp-project.iam.gserviceaccount.com" \
  --role="roles/bigquery.dataEditor"

Example Queries

-- Events by type in the last 24 hours
SELECT event_type, COUNT(*) as count
FROM `my-gcp-project.grantex.events`
WHERE PARSE_TIMESTAMP('%Y-%m-%dT%H:%M:%SZ', created_at) > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 24 HOUR)
GROUP BY event_type
ORDER BY count DESC;

-- All grant revocations with cascade details
SELECT
  event_id,
  created_at,
  JSON_VALUE(data, '$.grantId') AS grant_id,
  JSON_VALUE(data, '$.cascade') AS cascade
FROM `my-gcp-project.grantex.events`
WHERE event_type = 'grant.revoked'
ORDER BY created_at DESC
LIMIT 100;

-- Agents with the most token issuances
SELECT
  JSON_VALUE(data, '$.agentId') AS agent_id,
  COUNT(*) AS tokens_issued
FROM `my-gcp-project.grantex.events`
WHERE event_type = 'token.issued'
GROUP BY agent_id
ORDER BY tokens_issued DESC
LIMIT 20;

Multi-Destination Setup

For comprehensive compliance, send events to both a SIEM (for real-time alerting) and a data warehouse (for long-term retention):

import {
  EventSource,
  DatadogDestination,
  S3Destination,
  BigQueryDestination,
} from '@grantex/destinations';

const source = new EventSource({
  url: 'https://api.grantex.dev',
  apiKey: process.env.GRANTEX_API_KEY!,
});

// Real-time alerting
source.addDestination(new DatadogDestination({
  apiKey: process.env.DD_API_KEY!,
  batchSize: 50,
  flushIntervalMs: 5000,
}));

// Long-term archival (S3)
source.addDestination(new S3Destination({
  bucket: 'my-company-grantex-archive',
  prefix: 'events',
  region: 'us-east-1',
  batchSize: 1000,
  flushIntervalMs: 60000,
}));

// Analytics (BigQuery)
source.addDestination(new BigQueryDestination({
  projectId: 'my-gcp-project',
  datasetId: 'grantex',
  tableId: 'events',
  batchSize: 500,
  flushIntervalMs: 30000,
}));

await source.start();

Events are dispatched to all destinations concurrently. A failure in one destination does not block the others.

Compliance Best Practices

Retention Periods

Align your retention periods with your compliance framework:

Framework	Minimum Retention	Recommendation
SOC 2	1 year	3 years
HIPAA	6 years	7 years
GDPR	As needed	3 years (with deletion capability)
PCI DSS	1 year	3 years
FedRAMP	3 years	5 years

Immutability

Enable object lock on your S3 bucket to prevent deletion or modification of archived events:

aws s3api put-object-lock-configuration \
  --bucket my-company-grantex-events \
  --object-lock-configuration '{
    "ObjectLockEnabled": "Enabled",
    "Rule": {
      "DefaultRetention": {
        "Mode": "COMPLIANCE",
        "Years": 3
      }
    }
  }'

Encryption

S3: Enable SSE-S3 or SSE-KMS default encryption on your bucket
BigQuery: Data is encrypted at rest by default; use CMEK for additional control

Access Controls

Use dedicated IAM roles with least-privilege permissions
Enable CloudTrail (AWS) or Audit Logs (GCP) on the archival resources
Restrict access to the archival bucket/dataset to compliance and security teams

Completeness Verification

Periodically verify that your archive contains all expected events:

-- BigQuery: compare event count with Grantex audit log
SELECT
  DATE(PARSE_TIMESTAMP('%Y-%m-%dT%H:%M:%SZ', created_at)) AS day,
  COUNT(*) AS event_count
FROM `my-gcp-project.grantex.events`
GROUP BY day
ORDER BY day DESC
LIMIT 30;

Cross-reference these counts against the Grantex audit log (GET /v1/audit/entries) to confirm no events were lost.

Graceful Shutdown

Ensure buffered events are flushed before your process exits:

process.on('SIGTERM', async () => {
  await source.stop();  // flushes all destinations and closes connections
  process.exit(0);
});

Next Steps

Event Streaming — SSE/WebSocket architecture overview
Datadog Integration — real-time alerting with Datadog
Splunk Integration — search and dashboards with Splunk
Metrics & Observability — Prometheus metrics and Grafana dashboards

Getting Started

Core Concepts

Trust & Identity

Offline Authorization

x402 Payment Protocol

MCP Authorization

OACP Authority

Agentic Commerce V1 (Historical)

Compliance

Guides

Case Studies

Examples

S3 & BigQuery Archival

Prerequisites

Amazon S3

Setup

Configuration Options

How It Works

IAM Policy

S3 Lifecycle Policy

Querying with Athena

Google BigQuery

Setup

Configuration Options

How It Works

Table Schema

IAM Permissions

Example Queries

Multi-Destination Setup

Compliance Best Practices

Retention Periods

Immutability

Encryption

Access Controls

Completeness Verification

Graceful Shutdown

Next Steps

​Prerequisites

​Amazon S3

​Setup

​Configuration Options

​How It Works

​IAM Policy

​S3 Lifecycle Policy

​Querying with Athena

​Google BigQuery

​Setup

​Configuration Options

​How It Works

​Table Schema

​IAM Permissions

​Example Queries

​Multi-Destination Setup

​Compliance Best Practices

​Retention Periods

​Immutability

​Encryption

​Access Controls

​Completeness Verification

​Graceful Shutdown

​Next Steps

Prerequisites

Amazon S3

Setup

Configuration Options

How It Works

IAM Policy

S3 Lifecycle Policy

Querying with Athena

Google BigQuery

Setup

Configuration Options

How It Works

Table Schema

IAM Permissions

Example Queries

Multi-Destination Setup

Compliance Best Practices

Retention Periods

Immutability

Encryption

Access Controls

Completeness Verification

Graceful Shutdown

Next Steps