Top 30 AWS Certified Database – Specialty Interview Questions on Database Migration

Database migration is the process of transferring data and applications from one database system to another. The migration process involves moving the schema, data, and other database objects from the source database to the target database.
Database migration is important for several reasons, including:
Improved performance: Newer database systems often have more advanced features and better performance than legacy systems. By migrating to a modern database system, you can take advantage of these features and improve application performance.
Cost savings: Migrating to a newer database system can help reduce costs by using more efficient hardware and software, eliminating licensing fees for outdated systems, and reducing maintenance costs.
Increased scalability: Modern database systems are often designed to handle large amounts of data and support scaling both vertically and horizontally. Migrating to a modern database system can help ensure that your database can handle the growing needs of your business.
Better security: Modern database systems often have more advanced security features than legacy systems, including data encryption, access controls, and auditing capabilities. By migrating to a modern database system, you can improve the security of your database and protect sensitive data.
Regulatory compliance: Many industries and regions have specific regulations and compliance requirements for data storage and management. Migrating to a modern database system can help ensure that your database meets these requirements and avoids potential legal and financial penalties.
In summary, database migration is an important process for improving application performance, reducing costs, increasing scalability, improving security, and meeting regulatory compliance requirements. By migrating to a modern database system, organizations can take advantage of new features and capabilities, stay competitive, and ensure that their data is secure and compliant.

There are several types of database migration, including:
Schema Migration: This involves changes to the database schema, such as adding or removing tables, columns, constraints, and indexes.
Data Migration: This involves moving data from one database to another, or from one version of a database to another. Data migration can also involve transforming data to fit the new schema.
Application Migration: This involves migrating an application that depends on a database from one environment to another, such as from a development environment to a production environment.
Database Platform Migration: This involves migrating a database from one platform to another, such as from Oracle to MySQL.
Cloud Migration: This involves moving a database from an on-premise environment to a cloud environment, or from one cloud environment to another.
Database Consolidation: This involves combining multiple databases into a single database.
Database Splitting: This involves separating a single database into multiple databases.
Cross-Platform Migration: This involves migrating a database from one operating system to another, such as from Windows to Linux.
Each of these types of database migration requires different techniques and tools, and the complexity of the migration will depend on the specific scenario and the size and complexity of the database being migrated.

AWS Database Migration Service (DMS) is a fully managed service provided by Amazon Web Services (AWS) that enables users to migrate databases from one source to another, either on-premises or in the cloud. DMS supports both homogeneous and heterogeneous database migrations, meaning that it can migrate databases from one database engine to another, as well as from the same database engine.
DMS supports a variety of source and target databases, including Amazon RDS, Amazon Aurora, Amazon Redshift, and other on-premises or cloud-based databases such as Oracle, SQL Server, MySQL, MariaDB, and PostgreSQL.
DMS simplifies the migration process by providing a web-based console or APIs to manage and automate the entire migration process. It supports both one-time and ongoing replication, and it provides features such as schema conversion, data filtering, and continuous data replication.
DMS also allows users to monitor and troubleshoot database migration tasks using a dashboard that displays metrics and logs, and it provides notifications when tasks are completed or if errors are encountered.
Overall, AWS DMS provides a flexible and efficient solution for migrating databases between different environments with minimal downtime and minimal impact on application performance.

AWS Database Migration Service (DMS) is a fully managed service that makes it easy to migrate databases to and from the AWS Cloud. Here’s how it works:
Source and target database configuration: First, you need to configure the source and target databases that you want to migrate. This involves providing the necessary connection details, including the endpoint URL, port number, database name, and credentials for accessing the database.
Replication instance setup: Next, you create a replication instance, which is a compute and storage resource used to migrate data between the source and target databases. The replication instance is provisioned with the necessary resources, including CPU, memory, and storage, to handle the migration workload.
Task creation: Once the source and target databases and the replication instance are set up, you can create a migration task. The migration task defines the source and target databases, the replication instance, and any additional settings or options for the migration, such as table mappings, data transformation rules, and error handling.
Data replication: After the migration task is created, AWS DMS starts replicating data from the source database to the target database using the replication instance. AWS DMS uses a replication engine to capture changes to the source database and apply them to the target database in real-time or near-real-time.
Monitoring and troubleshooting: During the replication process, AWS DMS provides real-time monitoring and logging of the migration progress, including performance metrics, error messages, and alerts. You can use the monitoring data to troubleshoot any issues that arise during the migration process.
Migration completion: Once the data replication is complete, you can validate the data in the target database to ensure that it matches the source database. AWS DMS provides tools for data validation and comparison, as well as options for post-migration cleanups, such as deleting the replication instance and migration task.
In summary, AWS DMS makes it easy to migrate databases to and from the AWS Cloud by providing a fully managed service that automates many of the migration tasks, including data replication, monitoring, and troubleshooting.

AWS Schema Conversion Tool (SCT) is a tool provided by Amazon Web Services (AWS) that help customers convert their database schema from one database engine to another. The tool supports conversion from various on-premises and cloud-based databases to AWS databases, including Amazon RDS, Amazon Aurora, Amazon Redshift, and Amazon DocumentDB.
The AWS Schema Conversion Tool automates much of the process of converting database schemas, and it provides a user-friendly graphical interface that simplifies the conversion process. The tool supports the conversion of database objects, such as tables, indexes, and constraints, and it also supports the conversion of data types and functions.

The AWS Schema Conversion Tool also helps users identify any incompatibilities or potential issues that may arise during the migration process. For example, it can detect unsupported data types or functions in the source database, and suggest alternatives for the conversion.
In addition, SCT provides users with recommendations on how to optimize their database schema for AWS, including advice on database tuning, performance, and security.
Overall, the AWS Schema Conversion Tool helps simplify the process of migrating databases to AWS, reducing the time and effort required for the migration while also minimizing the risk of errors or data loss during the conversion process.

AWS Schema Conversion Tool (SCT) is a tool provided by AWS that helps in converting database schemas and applications from one database engine to another. The main purpose of AWS SCT is to simplify the process of database migration by automatically converting database schemas and associated code from one database engine to another.
Here are some key purposes of AWS SCT:
Schema conversion: AWS SCT can automatically convert database schema definitions and code from one database engine to another. This makes it easier to migrate databases from one engine to another, without having to manually rewrite the schema and code.
Code conversion: AWS SCT can also convert stored procedures, functions, and triggers from one database engine to another. This ensures that the application code remains functional after the migration.
Data migration: AWS SCT can help migrate data from one database engine to another. This includes data mapping and transformation, ensuring that data is properly migrated and formatted to match the new database engine.
Assessment: AWS SCT can also provide an assessment of the complexity of the migration. This includes identifying any issues that may arise during the migration and providing recommendations on how to address them.
Integration with other AWS services: AWS SCT integrates with other AWS services such as AWS DMS, which helps to automate the entire database migration process.
In summary, AWS Schema Conversion Tool (SCT) simplifies the process of database migration by automatically converting database schema definitions and associated code from one database engine to another. It also assists in data migration, provides an assessment of the complexity of the migration, and integrates with other AWS services to automate the entire migration process.

Yes, AWS Schema Conversion Tool (SCT) can convert database code as part of the overall schema conversion process.
SCT can convert stored procedures, functions, triggers, and other database codes from the source database to the target database engine. It also supports code conversion from PL/SQL to T-SQL, and vice versa, as well as conversion from Oracle-specific syntax to Amazon Redshift-specific syntax.
SCT provides a comprehensive view of the code objects in the source database and allows users to select which objects they want to convert to the target database. It also generates conversion reports that show any errors, warnings, or information related to the code conversion.
To convert code, SCT analyzes the code in the source database and generates equivalent code for the target database engine. It also includes a built-in code editor that allows users to modify the generated code, test it, and then deploy it in the target database.
It’s important to note that while SCT can convert database code, it may not be able to convert all code objects or syntax from the source database to the target database. In some cases, manual modification of the code may be required to ensure that it runs correctly in the target database engine.

AWS Database Migration Service (DMS) supports a wide range of database engines, including:
Amazon Aurora
PostgreSQL
MySQL
MariaDB
Oracle
Microsoft SQL Server
MongoDB
IBM Db2
SAP ASE
SAP HANA
Amazon Redshift
In addition, AWS DMS also supports heterogeneous migrations, where the source and target databases are different types. For example, you can migrate data from an Oracle database to an Amazon Aurora database or from a Microsoft SQL Server database to an Amazon Redshift cluster.
It’s worth noting that the availability of a specific database engine may depend on the region you’re working in. You can check the AWS DMS documentation for the latest list of supported engines in your region.

Yes, AWS Database Migration Service (DMS) can be used for one-time database migrations. In fact, one-time migration is one of the common use cases for DMS.
With DMS, users can migrate a database from a source to a target environment, either on-premises or in the cloud, in a one-time operation. This can be useful when users want to move a database from one environment to another, such as moving from an on-premises environment to the cloud, or from one cloud environment to another.
DMS supports several migration types for one-time migrations, including full-load migration, which moves the entire contents of the source database to the target database in a one-time operation, and change data capture (CDC) migration, which captures changes to the source database and applies them to the target database in near real-time.
DMS also supports various migration scenarios for one-time migrations, including homogeneous and heterogeneous migrations, and it can migrate databases from a variety of source and target database engines, including Oracle, SQL Server, MySQL, MariaDB, PostgreSQL, and Amazon RDS.
Once the one-time migration is complete, users can switch their applications to use the target database and decommission the source database.
Overall, AWS DMS provides a reliable, secure, and efficient way to migrate databases in a one-time operation, while minimizing downtime and disruption to the business.

AWS Database Migration Service (DMS) is designed to handle large databases with ease. Here are some ways that AWS DMS handles large databases:
Parallel data transfer: AWS DMS can transfer data in parallel from multiple tables, schemas, or databases. This helps to speed up the migration process, even for large databases.
Continuous data replication: AWS DMS supports continuous data replication, which means that data changes are transferred to the target database as they occur in the source database. This ensures that the target database stays in sync with the source database and minimizes the downtime required for the final cutover.
Data compression: AWS DMS uses data compression techniques to reduce the amount of data that needs to be transferred over the network. This helps to speed up the migration process and reduce the overall cost.
Incremental data updates: AWS DMS can track changes made to the source database since the last replication and only transfer the incremental changes to the target database. This reduces the amount of data that needs to be transferred and speeds up the replication process.
Migration assessment: Before starting the migration process, AWS DMS provides a migration assessment that helps to identify potential issues and estimate the time required to complete the migration. This helps to ensure that the migration process is optimized for large databases and any potential issues are addressed in advance.
In summary, AWS DMS provides a range of features and optimizations that help to handle large databases with ease. These features help to speed up the migration process, reduce downtime, and minimize the overall cost of the migration.

Yes, AWS Database Migration Service (DMS) can replicate changes in real-time using its Change Data Capture (CDC) feature. CDC allows DMS to capture changes that occur in the source database and then replicate those changes in near real-time to the target database.
With CDC enabled, DMS continuously captures changes that occur in the source database, such as inserts, updates, and deletes, and then sends those changes to the target database. CDC can be used with both homogeneous and heterogeneous database migrations, meaning that it can replicate changes between databases that use the same database engine as well as between databases that use different database engines.
CDC works by reading the transaction logs in the source database to capture changes, so it can be used with any database engine that provides transaction logs, such as Oracle, SQL Server, and MySQL.
CDC has several benefits, including reducing the time required to migrate databases, minimizing the downtime and impact on application performance, and allowing for zero-downtime migrations.
Additionally, DMS provides users with monitoring and troubleshooting tools to ensure that CDC is working correctly, and it supports features such as filtering and mapping of data to ensure that only the desired data is replicated.
Overall, CDC enables AWS DMS to provide a reliable and efficient way to replicate changes in real-time, making it an excellent choice for organizations that need to move data between databases with minimal disruption to their business.

AWS Database Migration Service (DMS) offers several advantages for database migration:
Fully managed service: AWS DMS is a fully managed service, meaning that AWS takes care of the infrastructure, maintenance, and management of the service. This reduces the operational overhead for managing the migration process, allowing you to focus on other tasks.
Wide range of supported database engines: AWS DMS supports a wide range of database engines, including MySQL, PostgreSQL, Oracle, Microsoft SQL Server, Amazon Aurora, MariaDB, and more. This makes it easy to migrate databases between different engine types, without having to manually convert the schema and code.
Real-time data replication: AWS DMS provides real-time or near-real-time data replication, ensuring that changes made to the source database are replicated to the target database quickly and accurately.
High performance: AWS DMS is designed for high-performance database migration, allowing you to migrate large databases quickly and efficiently.
Secure: AWS DMS provides built-in security features, including encryption in transit and at rest, to ensure that data is protected during the migration process.
Flexible: AWS DMS provides flexible migration options, including full database migration, partial migration, and continuous data replication.
Integration with other AWS services: AWS DMS integrates with other AWS services, such as AWS Schema Conversion Tool (SCT) and AWS Database Query Accelerator (DQA), to automate the entire database migration process and optimize database performance.
In summary, AWS Database Migration Service (DMS) offers a fully managed, high-performance, and secure solution for database migration. With support for a wide range of database engines and flexible migration options, AWS DMS simplifies the migration process and integrates with other AWS services to optimize database performance.

Yes, AWS Database Migration Service (DMS) can replicate data across different AWS regions. In fact, DMS is designed to support cross-region database migrations, making it a flexible and powerful tool for organizations that need to move data between regions for backup, disaster recovery, or other purposes.
To replicate data across regions, users can launch DMS replication instances in both the source and target regions, and then configure the replication tasks to replicate the data between them. Users can select the source and target databases, choose the replication type, and set up any necessary mapping, filtering, or transformation of the data.
AWS DMS provides several benefits for cross-region replication, including:
High availability and scalability: DMS replication instances can be launched in multiple Availability Zones (AZs) in each region, providing high availability and fault tolerance. DMS can also automatically scale the replication instances up or down to handle changes in workload.
Efficient and cost-effective data transfer: DMS uses the AWS Global Network to transfer data between regions, which can provide faster and more cost-effective data transfer than using the public internet.
Encryption and security: DMS supports encryption of data at rest and in transit, as well as integration with AWS Identity and Access Management (IAM) for user authentication and authorization.
Monitoring and management: DMS provides users with monitoring and management tools, including metrics and logs, to ensure that the replication is working correctly and to troubleshoot any issues that may arise.
Overall, AWS DMS is a powerful and flexible tool for replicating data across different AWS regions, providing a reliable and efficient way to move data between regions while minimizing disruption to the business.

AWS Database Migration Service (DMS) uses several techniques to ensure data consistency during migration, depending on the type of migration and the source and target database engines involved. Some of the key techniques used by DMS include:
Validation: DMS uses built-in validation checks to ensure that data is consistent between the source and target databases. For example, DMS can validate the table schemas, primary key constraints, and data types between the source and target databases to ensure that the data is correctly mapped and transformed during migration.
Data mapping and transformation: DMS can map and transform data between different database engines to ensure that it is consistent and usable in the target database. DMS provides a mapping feature that allows users to customize the data transformation rules, such as renaming columns or changing data types.
Parallel data migration: DMS can migrate data in parallel to improve performance and minimize downtime. This helps ensure that all data is migrated consistently and without errors.
Change Data Capture (CDC): When using CDC, DMS captures changes to the source database in near real-time and applies them to the target database. This helps ensure that the data is consistent and up-to-date between the two databases.
Consistency checks: DMS can perform consistency checks during the migration process to ensure that the data in the target database match the source database. These checks can include comparing row counts, checksums, and other metrics to verify the accuracy and completeness of the data.
Overall, AWS DMS provides a comprehensive set of features and techniques to ensure data consistency during migration, reducing the risk of data loss or corruption and ensuring a successful migration with minimal disruption to the business.

Here are some best practices for using AWS Database Migration Service (DMS) for database migration:
Plan and test thoroughly: Before starting the migration, it is important to plan and test thoroughly. This includes identifying the scope of the migration, selecting the appropriate replication method, and testing the migration process in a non-production environment.
Use a dedicated replication instance: It is recommended to use a dedicated replication instance for database migration. This helps to ensure that the migration process runs smoothly and does not interfere with other processes running on the instance.
Optimize the source database: It is important to optimize the source database before starting the migration. This includes ensuring that the database is properly indexed and that the queries used for replication are optimized.
Monitor the migration: During the migration process, it is important to monitor the replication and target the database for any issues or errors. AWS DMS provides several monitoring tools to help with this, including CloudWatch metrics and replication instance logs.
Use AWS Schema Conversion Tool (SCT): AWS Schema Conversion Tool (SCT) can be used to automatically convert the schema and code from the source database engine to the target database engine. This helps to reduce the time and effort required for the migration.
Choose the appropriate replication method: AWS DMS provides several replication methods, including one-time migration, full-load migration, and ongoing replication. It is important to choose the appropriate replication method based on the size and complexity of the database.
Optimize the target database: After the migration is complete, it is important to optimize the target database for performance. This includes indexing the database and tuning the queries used for replication.
In summary, planning and testing thoroughly, using a dedicated replication instance, optimizing the source and target databases, monitoring the migration, using AWS Schema Conversion Tool (SCT), choosing the appropriate replication method, and optimizing the target database are some best practices for using AWS DMS for database migration.

Yes, AWS Schema Conversion Tool (SCT) can convert data types during schema For example, if the source database uses the data type “text” and the target database supports “varchar(max)”, AWS SCT can automatically map “text” to “varchar(max)” during the schema conversion process. However, if you prefer to use a different data type mapping, you can customize the mapping using the AWS SCT interface.

It’s important to note that AWS SCT cannot always automatically map data types between source and target databases, especially if there are significant differences between the two databases. In such cases, you may need to manually modify the schema or use a custom data type mapping.

To use AWS Schema Conversion Tool (SCT), there are several prerequisites that need to be met, including:
AWS account: You need an AWS account to use SCT.
IAM user with required permissions: You need an IAM user with the necessary permissions to use SCT. Specifically, the user needs permission to access the SCT service and to create and modify AWS resources, such as Amazon S3 buckets, Amazon RDS instances, and Amazon EC2 instances.
Source and target databases: You need access to the source and target databases that you want to convert or migrate. The source database can be on-premises or in the cloud, and the target database can be an AWS database, such as Amazon RDS or Amazon Redshift, or an on-premises database.
SCT installation: You need to download and install SCT on your local computer or a remote server. SCT is available for Windows, macOS, and Linux operating systems.
Network connectivity: You need to ensure that there is network connectivity between the source and target databases, as well as between your local computer or remote server and the AWS resources you will use with SCT, such as Amazon S3 buckets and Amazon RDS instances.
Database drivers and connectors: You need to have the appropriate database drivers and connectors installed for the source and target databases you are working with. SCT supports a variety of database engines, including Oracle, SQL Server, MySQL, PostgreSQL, and others.
S3 bucket: You need to create an Amazon S3 bucket to store the converted schema and any associated objects, such as scripts and logs.
By meeting these prerequisites, you can begin using AWS Schema Conversion Tool to convert and migrate your databases to AWS with confidence.

When AWS Schema Conversion Tool (SCT) encounters database objects that are not supported by the target database engine, it provides recommendations and options to help you handle these objects.
First, during the schema analysis phase, AWS SCT generates a report that identifies any unsupported objects or features in the source database schema. This report includes a description of the object or feature, the reason it’s not supported, and recommendations for how to handle it.
For unsupported database objects, AWS SCT provides several options:
Ignore the object: If the object is not required for the application to function properly, you can choose to ignore it. AWS SCT will not convert or migrate the object to the target database.
Convert the object to a compatible format: If the object can be converted to a format that is supported by the target database, AWS SCT will provide recommendations on how to perform the conversion.
Customize the conversion: If you have a specific requirement or approach for how to handle the unsupported object, you can customize the conversion using AWS SCT’s user interface or the command-line interface. You can modify the conversion rules and scripts to convert the object to a format that is supported by the target database.
Create the object manually: If the object is required for the application to function properly and cannot be converted or customized, you may need to create the object manually in the target database after the migration is complete.
In summary, AWS SCT provides several options to handle unsupported database objects during the migration process. It’s important to carefully evaluate the options and choose the best approach for your specific requirements and application.

Yes, AWS SCT can generate code for the target database engine, including the schema and application code. It can also create a migration assessment report to help identify issues and provide recommendations.

AWS SCT can reduce the complexity and time required for database migration by automatically converting the schema and code of the source database. It can also help identify and address compatibility issues and reduce the risk of data loss or corruption.

AWS DMS can be used to perform zero-downtime migration by replicating changes from the source database to the target database in real time. It can also be used to perform near-zero downtime migration by using a combination of replication and data validation techniques.

Yes, AWS Database Migration Service (DMS) can be used to migrate data between on-premises databases and cloud-based databases.
AWS DMS supports both on-premises and cloud-based database sources, including databases hosted on Amazon EC2 instances, databases hosted on-premises, and databases hosted on other cloud platforms. AWS DMS can migrate data from these sources to cloud-based database targets, such as Amazon RDS, Amazon Aurora, Amazon Redshift, and Amazon DocumentDB.

To use AWS DMS to migrate data between on-premises and cloud-based databases, you need to set up an AWS DMS replication instance in the AWS Cloud and configure it to connect to the source and target databases.
AWS DMS supports several replication methods, including one-time migration, full-load migration, and ongoing replication, and provides several options for optimizing and monitoring the migration process.

In summary, AWS Database Migration Service (DMS) can be used to migrate data between on-premises and cloud-based databases and provides several replication methods and options for optimizing and monitoring the migration process. (DMS) can be used to migrate data between on-premises databases and cloud-based databases.
AWS DMS supports both on-premises and cloud-based database sources, including databases hosted on Amazon EC2 instances, databases hosted on-premises, and databases hosted on other cloud platforms. AWS DMS can migrate data from these sources to cloud-based database targets, such as Amazon RDS, Amazon Aurora, Amazon Redshift, and Amazon DocumentDB.
To use AWS DMS to migrate data between on-premises and cloud-based databases, you need to set up an AWS DMS replication instance in the AWS Cloud and configure it to connect to the source and target databases.
AWS DMS supports several replication methods, including one-time migration, full-load migration, and ongoing replication, and provides several options for optimizing and monitoring the migration process.
In summary, AWS Database Migration Service (DMS) can be used to migrate data between on-premises and cloud-based databases and provides several replication methods and options for optimizing and monitoring the migration process.

Database migration is a complex process that involves moving data from one database to another, and it can be challenging in several ways. Here are some of the challenges associated with database migration:
Data quality issues: Data quality is critical in any database migration project, and it can be a significant challenge to ensure that the data being moved is accurate, complete, and consistent. Incomplete or inconsistent data can lead to errors in the migration process, which can result in application downtime, data loss, or corrupted data.
Data compatibility issues: The source and target databases may have different data types, data structures, and data storage formats, which can lead to data compatibility issues. This can make it difficult to move data between databases and may require data transformation, mapping, or conversion.
Downtime: The migration process may require downtime for the application or the database, which can impact business operations. Downtime can also result in the loss of business and productivity, especially if the migration process takes longer than expected.
Security and compliance: Data security and compliance are critical considerations in any database migration project, and failure to adhere to security and compliance requirements can lead to legal and financial penalties. It is essential to ensure that the data being migrated is encrypted and that the migration process adheres to regulatory compliance requirements.
Performance issues: Migrating a large database can be a time-consuming and resource-intensive process, and it can cause performance issues if not handled correctly. It is essential to ensure that the migration process does not adversely impact the performance of the application, the database, or the network.
Cost: Database migration can be a costly process, both in terms of time and resources. It is essential to plan the migration process carefully and consider the cost implications before starting the migration project.

Some best practices include performing a thorough analysis of the source and target database, optimizing the source database for migration, testing the migration process thoroughly, creating a fallback plan, and monitoring the migration process closely.

AWS DMS can help reduce the risk of data loss during migration by using transactional consistency and validation checks to ensure that data is migrated accurately and consistently.

Yes, AWS SCT can be used to convert applications that use proprietary database functions, but it may require additional manual steps to ensure compatibility with the target database engine.

AWS DMS (Database Migration Service) is a fully managed service provided by Amazon Web Services (AWS) that enables you to migrate your databases to AWS easily and securely. Here are some advantages of using AWS DMS for ongoing data replication:
Real-time data replication: With AWS DMS, you can replicate data continuously in real time from one database to another, making it easy to keep multiple databases in sync. This can be particularly useful for applications that require high availability, low latency, or real-time data analytics.
Fully managed service: AWS DMS is a fully managed service, meaning that AWS takes care of all the backend infrastructure, so you don’t have to worry about maintaining or scaling it. This can save your team time and resources that would otherwise be spent on setting up and managing a replication solution.
Supports a wide range of databases: AWS DMS supports a wide range of databases, including Amazon Aurora, Oracle, SQL Server, MySQL, PostgreSQL, and others. This makes it easy to migrate and replicate data from a variety of sources.
High performance: AWS DMS is designed for high performance and can handle large data volumes and high throughput with low latency. It also supports parallel replication, which can speed up data replication even further.
Data transformation: AWS DMS includes a variety of data transformation capabilities, including filtering, mapping, and conversion, which can help you migrate and replicate data more efficiently and accurately.
Data migration with minimal downtime: With AWS DMS, you can migrate your databases with minimal downtime by using features like CDC (Change Data Capture) and AWS DMS Replication Task. This can reduce the impact on your users and enable you to migrate and replicate data without disrupting business operations.

AWS SCT can help reduce the complexity of database migration by automatically converting the schema and code of the source database, identifying and addressing compatibility issues, and providing migration assessment reports.

Some key considerations include compatibility with the source and target database engines, scalability, ease of use, support for migration assessment and planning, and cost.

AWS DMS can help reduce the cost of database migration by automating many of the manual steps required for migration, reducing downtime and minimizing the risk of data loss or corruption. It can also help optimize the source database for migration, reducing the need for additional hardware or infrastructure.