Database Evolution

AI-Powered Acceleration: Harness Generative AI to slash migration workload by 2-3x, ensuring accurate end-to-end execution of your database transformation.

Flexible Security Solutions: Deploy Caylent AI securely within your own environment using Terraform automation, or leverage our protected infrastructure for seamless migration.

Expertise Meets Evolution: With hundreds of annual database migrations under our belt, our seasoned teams expertly navigate complex migration scenarios.

Fully Integrated Open Database: Experience a seamlessly operational Aurora or Redshift database, perfectly integrated into your existing environment.

Comprehensive Translation Validation: Receive detailed automated testing results, empowering you to verify the precision of your data migration.

Empowerment Through Knowledge: Benefit from extensive operational documentation and AWS-partnered training, equipping your team to excel with your new database platform.

GenAI-assisted database migration leverages Generative AI technologies to automate and optimize the process of moving data from one database system to another. This emerging approach uses AI to handle complex tasks such as schema conversion, stored procedure translation, and data validation, significantly reducing manual effort and potential errors.

GenAI enhances traditional migration methods by automating complex conversions, especially for stored procedures and custom functions. It can analyze and translate database structures more quickly and accurately than manual methods, adapt to various database types, and even suggest optimizations for the target database environment.

GenAI is particularly effective in modernizing legacy databases for cloud environments. It can automatically refactor database structures and code to take advantage of cloud-native features, optimize for performance in distributed systems, and help translate between different SQL dialects used by on-premises and cloud databases.

While GenAI offers significant benefits, it's crucial to ensure data security during the migration process. Key considerations include: using AI models that can operate within secure environments, ensuring data doesn't leave controlled infrastructures, and implementing robust testing and validation processes to verify the accuracy and integrity of migrated data.