The cloud landscape is evolving rapidly, driven by a need to simplify complex migrations and optimize data management for scalability and innovation. AI-driven cloud innovation is playing a pivotal role, with technologies such as AI-driven automation and advanced metadata handling reshaping how enterprises approach modernization.

By addressing challenges in migrating legacy applications and leveraging data for AI, businesses are finding new efficiencies and capabilities in the cloud. Amazon Web Services Inc. is at the forefront of these efforts, providing innovative tools, such as Amazon Q Developer and S3 Tables, to simplify migrations and optimize data management for scalable, AI-driven solutions.

AWS’ Mai-Lan Tomsen Bukovec talks about AI-driven cloud innovation with theCUBE.

“In my area, we are all about changing how people think about solving problems, and we’re doing that in two ways … in data and in migration of Windows applications, VMware applications and event mainframes,” said Mai-Lan Tomsen Bukovec (pictured), vice president of technology at AWS.

Tomsen Bukovec spoke with theCUBE Research’s Dave Vellante for theCUBE’s “Cloud AWS re:Invent Coverage,” during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed AI-driven cloud innovation and how AWS simplifies cloud migrations and data management to streamline processes, as well as enable businesses to optimize scalability and unlock the potential of their data.

AI-driven cloud innovation: Simplifying complex migrations

Q Developer is a transformative tool designed to ease the migration of legacy systems. For example, Signaturit Group, a European provider of comprehensive and highly secure digital transaction solutions, used an early version of Q Developer to migrate a Windows framework application. Doing so enabled the company to significantly reduce timelines, according to Tomsen Bukovec.

“They had estimated it would take them eight months, and they were able to reduce it to just a few days,” she said.

The tool’s AI capabilities also address specific challenges of moving to the cloud, which can be highly complex. AI enables seamless integration between VMware and AWS networking environments, for instance, cutting down the migration timeline dramatically.

“Customers have told us and our early testing has told us that just that super complicated part about networking for VMware, we reduce the time to do it by 80 times,” Tomsen Bukovec said. “It’s a huge benefit.”

Toyota Motor in North America used Q Developer to modernize 40-year-old mainframe applications integral to its supply chain, a critical system supporting the movement of parts and vehicles across its network. These legacy mainframes, built on COBOL, posed significant challenges due to their complexity and the dwindling pool of experts familiar with the technology. By leveraging Q Developer’s AI-driven capabilities, Toyota was able to document system architectures, decompose monolithic designs into manageable modules and create actionable cloud migration plans in a fraction of the expected time. This transformation not only reduced projected timelines, but also unlocked new efficiencies critical to maintaining a competitive edge in the automotive industry, Tomsen Bukovec explained.

“They were told it would take years and billions of dollars. What we’ve done with Q is we’ve brough AI to the table, we’ve brought AI to the team,” she said. “They said that just the definition of those COBOL modules, the documentation and the migration plan for them would’ve taken months. Now it’s down to days.”

Innovating data management for AI readiness

AWS also unveiled advancements in data management, such as S3 Tables and S3 Metadata, to support the growing need for scalable and interoperable data solutions. These technologies simplify managing large-scale data lakes and ensure compatibility with diverse compute engines. S3 Tables, which integrate with Apache Iceberg, allow businesses to run SQL queries and leverage AI more efficiently.

“The next generation of data lakes … they’re going to be on object metadata, because customers are going to run SQL queries to find the data they need for AI,” Tomsen Bukovec said. “They’re going to need to find the data that they need for knowledge bases or analytics. That is going to be the next generation of data lakes.”

The capability of Q for QuickSight working with S3 Tables, which is currently in preview, brings natural language querying to data analytics, according to Tomsen Bukovec. This capability empowers business users to build intuitive dashboards and extract insights without needing technical expertise.

“Your business users can build the most beautiful dashboards and they can understand the data they need so easily and simply with these new modifications,” she said.

AWS’ continuous focus on customer-driven innovation ensures that 90% of its roadmap comes from user feedback, creating tools that address real-world challenges. With these developments, businesses can streamline both their migrations and their ability to leverage data, paving the way for transformative growth in the cloud era, Tomsen Bukovec added.

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s “Cloud AWS re:Invent Coverage”:

Photo: SiliconANGLE