Database engines with appropriate use cases (for example, heterogeneous migrations, homogeneous migrations)

Use Cases:

• Homogeneous Migrations: When moving from an on-premises relational database to AWS, Amazon RDS is the service of choice for a homogeneous migration (i.e. Oracle to Amazon RDS for Oracle, MySQL to Amazon RDS for MySQL).
• Heterogeneous Migrations: RDS also supports heterogeneous migrations such as moving from a commercial database like Microsoft SQL Server or Oracle to an open-source database such as Amazon Aurora or PostgreSQL.

Features:

• Managed service, simplifying administration tasks such as hardware provisioning, database setup, patching, and backups.
• Multi-AZ deployments for high availability and Read Replicas for scalability.

Amazon Aurora

Use Cases:

• High-Throughput Workloads: Designed for high performance and availability, it is suitable for applications that demand higher performance than traditional relational databases.
• Scalability Requirements: When an application requires scaling beyond the constraints of a single RDS instance.

Features:

• Compatible with MySQL and PostgreSQL, enabling easy migration from these databases.
• Up to five times the throughput of standard MySQL and three times that of standard PostgreSQL.
• Automated failover, backup, and recovery.

Amazon DynamoDB

Use Cases:

• Schema-less and NoSQL Workloads: For applications requiring fast, consistent, and single-digit millisecond latency irrespective of scale.
• Server-less Applications: Can be used in conjunction with AWS Lambda for fully managed server-less applications.

Features:

• Fully managed NoSQL database service.
• Integrated with AWS Lambda for triggering functions on data changes.
• Supports both document and key-value store models.

Amazon Redshift

Use Cases:

• Data Warehousing and Big Data: Ideal for running complex, large-scale queries on datasets for business intelligence and data analytics.
• Log Analytics: Suitable for analyzing and storing application and access logs at scale.

Features:

• Columnar storage and massively parallel processing (MPP) architecture for fast query performance.
• Integration with popular business intelligence tools.
• Automated backups and snapshots.

Amazon DocumentDB

Use Cases:

• MongoDB Workloads: Suitable for users who want MongoDB-compatible database solutions managed by AWS.
• Document Store Applications: Ideal for content management, catalogs, and user profiles where document storage is preferred.

Features:

• Compatibility with the MongoDB API.
• Storage is automatically scaled as the size of the database grows.
• Automated backup and point-in-time recovery.

Amazon Neptune

Use Cases:

• Graph Database Needs: For applications that need to work with highly connected data, such as social networking, recommendation engines, and fraud detection systems.
• Knowledge Graphs: Useful for applications that require reasoning across a wide swath of information.

Features:

• Supports popular graph models like Property Graph and W3C’s RDF.
• High query performance for complex graph queries.

Elasticache

Use Cases:

• Caching Needs: For applications that need a high-performance, in-memory caching layer to reduce latency when accessing data.
• Session Store: For managing session state in web applications at scale.

Features:

• Supports Redis and Memcached.
• Seamlessly scales out/in to handle the cache workload.

When considering a database engine for migration, AWS provides a range of tools:

• AWS Database Migration Service (DMS): Helps with both homogeneous and heterogeneous database migrations, ensuring minimal downtime.
• AWS Schema Conversion Tool (SCT): Facilitates heterogeneous migrations by converting the source database schema and a majority of the database code objects to a format compatible with the target database.

For example, migrating from an Oracle database to Amazon Aurora with PostgreSQL compatibility might involve using DMS for data replication and SCT for schema conversion.

In summary, each database engine serves specific use cases, and understanding these is key to a successful migration whether homogeneous or heterogeneous. AWS provides a suite of tools and services to make these transitions as seamless as possible while ensuring data integrity and minimizing downtime.