Exploring AWS !!

Day 41:

Amazon Aurora — Part 2

Aurora Replicas Autoscaling: scale the replicas based on the target value by adding or removing them. Best suitable for predictable workloads.

Aurora — Custom Endpoints

• Define a subset of Aurora Instance as a custom endpoint
• Example: Run analytical queries or specific replicas
• The reader endpoint is generally not used after defining custom endpoints.

Aurora Serverless:

• Automated database instantiation and autoscaling based on actual usage.
• Good for infrequent, intermittent or unpredictable workloads.
• No explicit planning needed.
• Pay per second, can be more cost effective.

Aurora Multi-Master:

• In case you want an immediate failover for write node (High Availability)
• Every node does Read/Write vs promoting a Read Replica as a new master.

Global Aurora:

• Aurora Cross Region Read Replicas: Useful for disaster recovery Simple to put in place
• Aurora Global Database (recommended): 1 Primary Region (read/write) Upto 5 secondary (read only) regions, replication lag is less than 1 second. Upto 16 read replicas per secondary region. Helps for decreasing latency. Promoting another region (for disaster recovery) has an RTO (Recovery Time Objective) of < 1 minute

Aurora Machine Learning:

• Enables you to add Machine Learning based predication to your applications via SQL.
• Simple, optimized and secure integration between Aurora and AWS Machine Learning services.
• Supported services: Amazon SageMaker (use with any ML model) Amazon Comprehend (for sentimental analysis)
• Don’t need to have ML experience.
• Use Case: Fraud detection, ads targeting, sentiment analysis, product recommendation