AWS Schema Conversion Tool: Migrate Your Database Easily

Migrating databases to the cloud can be a complex and challenging process, often requiring significant manual effort and specialized expertise. The AWS Schema Conversion Tool (SCT) is designed to simplify this process by automating much of the schema conversion work, making database migrations more predictable and cost-effective. This comprehensive guide explores the capabilities, benefits, and use cases of the AWS Schema Conversion Tool, helping you understand how it can streamline your cloud migration journey and optimize database performance.

What is the AWS Schema Conversion Tool (SCT)?

The AWS Schema Conversion Tool (SCT)

is a powerful tool provided by Amazon Web Services (AWS) designed to automate the conversion of database schemas and code objects from one database platform to another. It simplifies heterogeneous database migrations by automatically converting the source database schema, a majority of the database code objects (including views, stored procedures, and functions), to a format compatible with the target database.

Any objects that cannot be automatically converted are clearly marked to make manual conversions easier. SCT can scan your application source code for embedded SQL statements and convert them as part of a database schema conversion project. During this process, SCT performs cloud-native code optimization by converting legacy Oracle and SQL Server functions to their equivalent AWS service, thereby helping you modernize the application at the same time as database migration. Once schema conversion is complete, SCT can help migrate data from a range of data warehouses to Amazon Redshift using built-in data migration agents. The AWS Schema Conversion Tool supports various conversions, including moving schemas from Oracle, SQL Server, and other database platforms to Amazon RDS and Amazon Redshift.

The AWS Schema Conversion Tool is your ally in making database migrations smoother, predictable, and more cost-effective. It helps you move from legacy databases to modern, cloud-native solutions, thereby improving overall efficiency and scalability of your applications. SCT's capabilities extend to identifying and flagging compatibility issues between source and target databases, allowing for proactive remediation and risk mitigation. This leads to faster and more reliable database migrations, as well as reduced downtime and lower overall project costs. The process can often be broken down to several steps:

Key Benefits of Using AWS SCT

There are several reasons why companies are now choosing to use AWS SCT for their data migration purposes. Here are some key benefits when using AWS SCT:

• Reduced Migration Costs: By automating a significant portion of the schema conversion process, AWS SCT reduces the need for manual intervention, lowering overall migration costs.
• Simplified Migration Process: SCT simplifies the complexities of database migration, making it more accessible to organizations without deep database expertise.
• Improved Migration Speed: The automated conversion capabilities of SCT accelerate the migration timeline, allowing organizations to realize the benefits of cloud databases more quickly.
• Enhanced Database Compatibility: SCT ensures compatibility between the source and target databases, minimizing potential issues and ensuring smooth operation after migration.
• Cloud Native Optimization: SCT optimizes converted schemas and code objects for the AWS cloud environment, maximizing performance and scalability.
• Support for Multiple Databases: SCT supports a wide range of source and target databases, providing flexibility for different migration scenarios.
• Comprehensive Assessment Reports: SCT generates detailed assessment reports, identifying potential migration issues and providing recommendations for resolution.

Source Databases

The AWS Schema Conversion Tool supports a wide array of source databases, ensuring flexibility for various migration scenarios.

These include:

• Oracle Database
• Oracle Data Warehouse
• Azure SQL
• Microsoft SQL Server
• Teradata
• IBM Netezza
• Greenplum
• HPE Vertica
• MySQL and MariaDB
• PostgreSQL
• Amazon Aurora
• IBM DB2 LUW
• Apache Cassandra
• SAP ASE

Target Databases on Amazon RDS

When migrating to AWS, the Schema Conversion Tool provides support for these target databases on Amazon RDS:

• Amazon Aurora
• MySQL
• PostgreSQL
• Oracle
• Amazon Redshift

Homogeneous and Heterogeneous Database Migrations

AWS SCT supports both homogeneous and heterogeneous database migrations, providing flexibility for various migration scenarios:

• Homogeneous Migration: Involves migrating databases of the same type. For example, migrating from SQL Server to SQL Server on Amazon RDS. Homogeneous migrations are typically simpler because the schema and code objects are generally compatible.
• Heterogeneous Migration: Involves migrating databases of different types. For example, migrating from Microsoft SQL Server to MySQL RDS. Heterogeneous migrations require schema and code object conversion to ensure compatibility with the target database.

Step 1: Download and Install AWS SCT

First, you need to download and install the AWS Schema Conversion Tool on your local computer. You can download the tool from the AWS website. AWS SCT supports several platforms, so make sure you select the one that fits your OS, including:

• Microsoft Windows
• Apple Mac
• Fedora Linux
• Ubuntu Linux

Step 2: Create an AWS SCT Project

Once the tool is installed, you need to create an AWS SCT Project. Open the application and set up a project. You will need to have your AWS credentials and configure the source and target databases connections, where you will specify:

• Project Name
• Source Database Details
• Target Database Details

Step 3: Analyze the Source Database

Next, connect to the source database using AWS SCT and let the tool analyze the source. This step involves AWS SCT examining the schema, data types, code objects, and dependencies of the source database. The assessment report provides insights into potential migration challenges and compatibility issues.

Step 4: Convert the Schema

After the analysis, AWS SCT can automatically convert the source database schema to be compatible with the target database. During the conversion, AWS SCT creates equivalent database objects in the target database that match the source objects. It also identifies objects that cannot be automatically converted, highlighting them for manual conversion.

Step 5: Apply the Converted Schema to Target Database

You can apply the converted schema to the target database. AWS SCT provides options to review and modify the converted schema before applying it to the target database. Verify the converted objects, data types, and code, and adjust as required.

Step 6: Migrate Data

Once the schema is applied to the target database, you can migrate the data. AWS SCT can help migrate data from the source database to the target database. Depending on your data volume and migration strategy, you may want to perform a full load or incremental data migration.

Step 7: Test and Validate

After migration, it’s important to test and validate the migrated data and applications to ensure that everything works as expected. You can run test queries, conduct performance testing, and validate application functionality to confirm the migration was successful.

Cost Comparison: SQL Server vs. MySQL on AWS RDS

When considering a database migration to AWS RDS, understanding the pricing differences between database engines is critical.

The presenter mentioned a scenario where migrating from Microsoft SQL Server to MySQL on AWS RDS could result in significant cost savings. SQL Server may be up to five times more expensive than MySQL.

Here’s an explanation of potential cost savings, along with real numbers to help understand it. The pricing calculator and other tools can help estimate these numbers. By comparing the On-Demand DB Instance pricing for different database types, you can identify cost-effective migration options. This can often be broken down into the following areas:

Factors Influencing RDS Pricing

Several factors influence the pricing of AWS RDS instances. The key factors influencing Amazon RDS pricing include:

• Instance Type: Different instance types (e.g., db.t3.micro, db.m5.xlarge) offer varying compute, memory, and I/O resources, impacting the hourly cost.
• Database Engine: Different database engines (e.g., MySQL, PostgreSQL, SQL Server) have different pricing models.
• Deployment Option: Single-AZ deployments are typically cheaper than Multi-AZ deployments, which provide higher availability and fault tolerance.
• Storage: The amount and type of storage (e.g., General Purpose SSD, Provisioned IOPS SSD) affect the overall cost.
• Region: AWS Regions have different pricing structures.
• On-Demand vs. Reserved Instances: On-Demand Instances are pay-as-you-go, while Reserved Instances offer discounted rates for a commitment period.

Tools to Estimate RDS Costs

AWS provides several tools to help you estimate the cost of using different RDS instances.

These tools include:

• AWS Pricing Calculator: This tool allows you to estimate the monthly costs of AWS services based on your specific usage patterns.
• TCO Calculator: This tool helps you calculate the total cost of ownership (TCO) of running your applications on AWS compared to on-premises environments.
• Economics Resource Center: This resource provides additional insights and resources for switching to AWS.

Automated Schema Conversion

The primary feature of AWS SCT is its ability to automatically convert database schemas from a source database engine to a target database engine. This includes converting tables, indexes, views, stored procedures, and other database objects, all done automatically to speed up the migration process.

Compatibility Assessment

AWS SCT provides a compatibility assessment feature that analyzes the source database schema and identifies any compatibility issues that may arise during the conversion process. The tool provides a report detailing the issues and recommends potential solutions. The compatibility assessment reduces the risk of errors during the schema conversion process.

Customizable Conversion Rules

AWS SCT enables users to define custom conversion rules to tailor the schema conversion process according to specific requirements. The tool supports conversion rule management with the option to create, modify, and apply rules, ensuring schema conversion results precisely match expectations. You have the ability to customize the conversion for specific requirements.

Data Migration Support

Once the schema conversion is complete, AWS SCT assists with the actual data migration process. The tool supports data migration from the source to the target database, either in a single batch or incrementally, providing flexibility depending on data volumes and constraints.

Integration with AWS DMS

AWS SCT works with AWS Database Migration Service (DMS) for data replication. When AWS SCT is integrated with AWS DMS, it extends the flexibility of migration and replication. AWS DMS handles data replication and ensures data consistency.

Migrating from Commercial to Open-Source Databases

Many organizations use AWS SCT to migrate their databases from commercial database engines like Oracle and SQL Server to open-source alternatives like MySQL and PostgreSQL. It is a common practice to move away from costly commercial databases. AWS SCT makes the migration affordable with open-source solutions.

Migrating from On-Premises to Cloud Databases

Organizations also use AWS SCT to migrate their databases from on-premises environments to cloud-based AWS databases. The cloud can offer benefits like scalability, availability, and cost savings. Using AWS SCT, you can move on-premise to cloud-based databases.

Migrating Data Warehouses to Amazon Redshift

AWS SCT supports data warehouse migration to Amazon Redshift, AWS's cloud-based data warehousing service. This tool simplifies moving your data warehouse to Amazon Redshift for business intelligence purposes.

Modernizing Legacy Applications

When modernizing legacy applications, AWS SCT is used to migrate the databases supporting those applications to modern database engines. In this regard, AWS SCT allows you to perform cloud-native code optimization.