Amazon OpenSearch Service

Key Features

1. Managed Elasticsearch & OpenSearch – Fully managed service for deploying, operating, and scaling OpenSearch and Elasticsearch clusters.

2. Scalability & Auto-Tuning – Supports auto-scaling, UltraWarm storage for cost-effective data retention, and cold storage for historical data.

3. Security – Integration with AWS IAM, fine-grained access control, VPC support, and encryption (at rest and in transit).

4. Observability – Includes OpenSearch Dashboards, real-time monitoring, trace analytics, and log ingestion.

5. Machine Learning Anomaly Detection – Built-in ML models for anomaly detection in logs and metrics.

6. Multi-AZ Deployments – Ensures high availability and disaster recovery with cross-cluster replication.

7. Data Ingestion Support – Supports Amazon Kinesis Data Firehose, AWS Lambda, and Logstash for real-time log and event streaming.

8. Integration with AWS Services – Works with AWS CloudTrail, AWS WAF, Amazon S3, and Amazon SNS.

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How It Works

1. Data Ingestion

   • Logs, metrics, or event data are ingested via AWS services like Kinesis Firehose, Lambda, or API Gateway.

2. Indexing & Storage

   • Data is indexed in OpenSearch or Elasticsearch format for full-text search, analytics, and visualizations.

3. Search & Querying

   • Query large datasets using RESTful API, OpenSearch Query DSL, or SQL-like queries.

4. Monitoring & Alerts

   • Use OpenSearch Dashboards for real-time insights, log analysis, and anomaly detection.

5. Scaling & Performance Optimization

   • Uses sharding, UltraWarm, and cold storage to optimize performance and cost.

6. Security & Access Control

   • Uses IAM, Cognito authentication, and encryption to protect sensitive data.

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Use Case Example

Scenario: A DevOps team wants to monitor application logs in real time to detect errors and anomalies.

1. Logs from EC2, Lambda, and ECS are streamed using Amazon Kinesis Data Firehose.

2. Amazon OpenSearch Service indexes logs for real-time search and filtering.

3. Alerts & Dashboards: OpenSearch Dashboards visualize error rates, latency, and request logs.

4. Anomaly Detection: ML-powered detection finds unusual patterns in logs.

5. Actions Taken: AWS Lambda triggers alerts via SNS to notify the DevOps team.

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Topics for AWS DVA-C02

• Architecture of Amazon OpenSearch Service

• Data Ingestion Methods (Kinesis Firehose, Logstash, Direct API Calls)

• Querying OpenSearch (DSL & SQL-based Queries)

• Scaling Strategies (Auto-scaling, UltraWarm, Cold Storage)

• Security (IAM, Cognito, Encryption, Fine-grained Access Control)

• Performance Optimization (Sharding, Index Management, Caching)

• Integrations with AWS Services (CloudWatch, S3, Lambda, SNS, WAF)

• Use Cases & Best Practices

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