Speed Up Database Modernizations with GenAI

Many companies are eager to reduce technical debt and cut licensing costs by migrating away from their current, expensive database systems. However, the technical challenges along with the financial and opportunity costs of such migrations often make them prohibitively expensive. After completing hundreds of migrations, our team decided there had to be a better way. The result of our efforts to simplify the migration process is Caylent Accelerate™ Database Modernization. This groundbreaking generative AI solution makes previously fiscally impossible database migrations possible and cost-effective.

We'll explore the challenges we’ve tackled head-on as part of our comprehensive data and analytics services and how Caylent Accelerate™ was purpose-built to overcome these obstacles.

The Motivation to Migrate

It's no secret that database licensing costs are a significant burden for many companies. According to The Business Research Company, organizations spend nearly $200 billion annually on database licenses. With such astronomical figures, it's no wonder that a primary motivator for database migration is to reduce these licensing costs. For example, many companies are looking to move away from expensive proprietary systems like SQL Server or Oracle to open-source alternatives like PostgreSQL and MySQL, which are free.

However, cost reduction isn't the only driving factor. Another compelling reason to migrate is the opportunity to shed technical debt associated with existing implementations. A report by Quod Orbis reveals that technical debt costs organizations over $2 trillion annually. This accumulation of outdated or suboptimal database structures and practices can significantly hinder an organization's efficiency and agility. 

Migrating to a new, faster, and less expensive database can be a crucial step in application modernization, allowing companies to improve performance, reduce costs, and enhance security simultaneously.

The Challenge with Database Migrations

While the benefits of database migration are clear, the process is far from simple. According to a Bloor Research study, 40% of migrations are delayed due to undiscovered complexity. The planning, design, coding, and testing required can extend the process to a year or more, making it a daunting undertaking for many organizations. This lengthy timeline and the technical intricacies often deter companies from embarking on much-needed migrations without expert guidance. That's why many organizations turn to experienced cloud migration services to navigate the following challenges effectively:

Logic Complexity

One of the most significant hurdles in database migration is the complexity of the existing logic. Database logic is often spread across stored procedures, functions, views, and many other types. Converting all of this logic to work with the new database's capabilities is generally a heavy lift, requiring extensive expertise and time.

Database systems use different syntax and languages for stored procedures and slightly different SQL dialects. While some tools, such as the AWS Schema Conversion Tool (SCT), can automate part of the job of porting, a significant and more complex portion still requires intervention by skilled experts.

Extensive Testing

Due to the technical complexity of database migrations, rigorous and thorough testing is essential. This is especially true if you're taking the opportunity to fix issues along the way (such as type inconsistencies, data formatting problems, or schema issues) to reduce technical debt. Ensuring all data is correctly transferred and all functions work as expected in the new environment is a time-consuming but critical process.

Knowledge Gaps

Successful conversions require data engineering teams to possess in-depth knowledge of both source and destination systems, a rare combination of skills. This dual expertise goes beyond understanding different SQL dialects. It includes grasping the source system's intricacies, the destination system's capabilities, and how to bridge the gap between them. 

The knowledge gap challenge manifests differently for in-house teams and external consultants. In-house Subject Matter Experts (SMEs) often need extensive upskilling in the target system and migration tools, extending project timelines. Consultants, while bringing broad expertise, may lack specific organizational knowledge, potentially increasing costs. Organizations must balance these factors, possibly combining targeted in-house training with strategic use of consultants to manage both time and budget constraints effectively. 

Dependencies

Database objects like stored procedures don't exist in isolation. They interact with other database objects, such as views and functions, which might also need modification during the migration process. These interdependencies can significantly complicate the migration process and increase the risk of overlooking critical components.

Powering Database Migrations with Generative AI

At Caylent, we've accumulated extensive experience in Generative AI and database migrations, having completed hundreds of projects across multiple industries, including Financial Services, Healthcare, Government, and Media/Entertainment. Through these experiences, we've come to several important conclusions:

• GenAI thrives on complex tasks with clear validation criteria, making it ideal for database migrations where success is easily measurable
• This perfect match between GenAI and database migrations enables us to tackle previously insurmountable migration challenges with unprecedented efficiency
• As foundational AI models continue to improve, our GenAI solutions for database migrations become even more powerful, opening up new possibilities for faster, more accurate, and cost-effective migrations
• Since GenAI can receive context and additional information, this allows the combination of the Caylent team with consultants who have vast experience with AI models to produce more accurate responses
• GenAI and database migrations can be a powerful combination, but they can also pose a problem. Testing the output of the models and ensuring the results are consistent is crucial for a smooth migration

More specifically, we've found that GenAI excels at converting database objects (like stored procedures, functions, and views) between database engines, and these results have only improved over time as new models are available in the market. This trend suggests we expect even further improvements in the future, making GenAI an increasingly powerful tool for database migrations.

Caylent Accelerate™: Bringing Gen AI to Database Migration

Building on our experiences and insights, we developed Caylent Accelerate™, a revolutionary tool that creates the framework to translate database objects (e.g., stored procedures, functions, views, triggers) between databases and test the results. We've incorporated our extensive experience with database migrations into the prompts and orchestration, leveraging cutting-edge AWS services to power its AI-driven migration capabilities.

At the heart of Caylent Accelerate™ are LLMs, hosted on Amazon Bedrock, which enable us to continuously improve and deploy our AI models. Bedrock provides the robust machine learning infrastructure we need to deploy Open Source models at scale. It allows us to efficiently manage the entire machine learning lifecycle, from data preparation to model deployment and monitoring. This means we can rapidly iterate on our AI models, incorporating new learnings and improvements as we gather more data from successful migrations.

Amazon Bedrock offers a selection of high-performing foundation models, upon which we have developed a configurable chain of prompts for our use case of database migration. By using Bedrock, we ensure that Caylent Accelerate™ is always powered by state-of-the-art language models, capable of understanding and translating complex database schemas and stored procedures across different SQL dialects.

This powerful combination allows us to continuously improve our AI models based on real-world migration data, rapidly deploy updated models without service interruption, scale our AI capabilities to handle migrations of any size, and ensure high accuracy and reliability in our automated translations. By harnessing these AWS services, Caylent Accelerate™ stays at the forefront of AI-powered database migration technology, offering our clients the most advanced and efficient migration solution available. It also ensures that the information shared with the model is entirely secure since it will run inside your private environment (different than calling outside APIs and services). 

Essential to the success of Caylent Accelerate™ translation is the validation of the database objects on the target platform. By first creating the database on the target platform, then creating the object on the new database as they are validated, we enable the parallel execution of the procedure on both the source and target. To aid in this process, we have developed an automated data validation process that covers scenarios for the use of the objects. 

For more examples of Caylent customers who have found success, check out our case studies page! 

How much does Caylent Accelerate™ accelerate database migrations?

We've focused on tuning the system to perform SQL Server to PostgreSQL migrations, and the results have been remarkable. Let's compare the traditional migration process for a database with 2,500 Stored Procedures with the Caylent Accelerate™-powered approach: