Adobe Experience Manager (AEM) as a Cloud Service is built for scalability
and agility, making it ideal for enterprises delivering personalized digital
experiences. However, as applications scale, the need for enhanced log
monitoring and visualization becomes more pressing. While Adobe provides
standard logging tools, teams often require more flexible and comprehensive
solutions—like Grafana—to gain full observability.
In this guide, we’ll explore how AEM Cloud logs can be exported to AWS S3,
processed, and ultimately visualized in Grafana for robust reporting and
alerting.
Architecture Overview
The integration pipeline includes the following steps:
Steps
# AEM as a Cloud Service generates logs.
# Logs are forwarded to an AWS S3 bucket using Adobe’s log forwarding feature.
# A log processing service (e.g., Fluentd, Logstash, or AWS Lambda) reads logs
from S3 and pushes them to a log aggregation tool like Grafana Loki or
Elasticsearch.
# Grafana visualizes the data and enables custom alerting and dashboards.
Step-by-Step Integration
1. Enable Log Forwarding to AWS S3
AEM allows you to configure external log destinations. One of the supported
destinations is Amazon S3, which provides a scalable, durable, and
cost-effective solution for log storage.
2. Set Up an S3 Log Processing Pipeline
Once logs are stored in S3, a processing component is required to transform
and forward them to Grafana-compatible data stores.
Options include:
AWS Lambda with S3 trigger: Automatically processes new logs and forwards them
to a log collector.
Fluentd or Logstash running on AWS EC2 or Fargate: Periodically pulls logs from
S3 and sends them to Loki or Elasticsearch.
3. Push Logs to Loki or Elasticsearch
Use your chosen processor to send parsed log data to:
Grafana Loki (for time-series-based log storage)
Elasticsearch (for full-text search and analytics)
Ensure that logs are structured and tagged appropriately (e.g., environment,
service, log level).
4. Configure Grafana
Add the log storage backend (Loki or Elasticsearch) as a data source in
Grafana. From there, you can:
- Create dashboards for operational monitoring
- Set up alerts on error thresholds, request patterns, or specific log events
- Drill down by environment, instance, or component
Benefits of This Architecture
✅
Separation of Concerns
Using AWS S3 as an intermediary decouples log ingestion from processing and
analysis. This improves scalability and allows for batch or real-time
processing.
✅
Reliable and Cost-Effective Storage
S3 offers high durability and lifecycle policies for managing log retention and
archiving, helping optimize costs.
✅
Enhanced Flexibility
The modular pipeline lets you swap out processing components or destinations
(e.g., move from Loki to OpenSearch) without disrupting the entire system.
✅
Rich Visualization and Alerts
Grafana provides robust visualization capabilities and integrates with alerting
systems like Slack, PagerDuty, and email for real-time notifications.
Final Thoughts
By introducing AWS S3 as a central log storage layer between AEM and Grafana,
teams gain flexibility, scalability, and powerful observability options.
Whether you want real-time log monitoring or deep-dive analytics, this
architecture provides a future-proof approach to managing AEM logs efficiently.
Reporting
To create reports and configure alerts for AEM as a Cloud Service logs using a
solution like Grafana, follow these key steps:
🔧 Step 1: Ensure Logs
Are Structured and Indexed
Before creating reports or alerts:
1. Logs from AEM must be forwarded and ingested into a searchable/loggable
store like:
Grafana Loki
Elasticsearch
2. Ensure logs are structured—use JSON formatting if possible—and include
fields like:
* `level` (INFO, WARN, ERROR)
* `timestamp`
* `service/component`
* `message`
* `environment` (author/publish)
---
📊 Step 2: Create
Dashboards in Grafana
1. Connect your data source:
* In Grafana, go to Settings → Data Sources.
* Add Loki or Elasticsearch depending on your backend.
2. Create a new dashboard:
* Go to Dashboards → New Dashboard.
* Add a panel with a query, for example:
* For Loki:
```logql
{app="aem", level="error"} |=
"Exception"
```
* For Elasticsearch:
Use Lucene query:
```
level:error AND message:*Exception*
```
3. Visualize with graphs or tables:
* Line charts for error trends over time
* Table view for detailed log entries
* Bar charts for per-component errors
🚨 Step 3: Configure
Alerting
In Grafana 9+ (Unified Alerting):
1. Open the panel where your log query is configured.
2. Click on “Alert” → “Create Alert Rule”.
3. Set the evaluation interval (e.g., every 1 min).
4. Define conditions:
* e.g., *“When count() of logs with level=ERROR is above 10 for 5
minutes”*
5. Add labels and annotations to identify the alert.
Example Alert Condition (Loki):
```yaml
expr: count_over_time({app="aem", level="error"}[5m]) >
10
```
6. Configure notification channels:
* Go to Alerting → Contact points
* Add:
* Email
* Slack webhook
* Microsoft Teams
* PagerDuty
* Opsgenie, etc.
* Associate contact points with your alert rule via notification policies
✅ Best Practices
Threshold tuning: Avoid alert fatigue by tuning thresholds carefully.
Environment separation: Create separate alerts for author and publish
environments.
Alert grouping: Group multiple errors or similar logs into a single
alert message to reduce noise.
Include context: Use annotations to include relevant log data or links
to dashboards in the alert message.
🎯 Example Use Cases
* Alert when error rate spikes (e.g., more than 50 errors in 10 minutes).
* Alert when specific patterns appear (e.g., `OutOfMemoryError`,
`SlingException`).
* Alert when log frequency drops (indicating system inactivity or crash).
* Dashboard shows errors by component (e.g., DAM, Forms, Dispatcher).
