Google’s Unified Data Platforms Transform Business Data and AI Integration

The trend toward unified data platforms that can handle diverse data types and offer seamless integration for AI workloads is transforming the way businesses manage and use their data.

This shift paves the way for more efficient, scalable and intelligent data ecosystems that can support the The Next Wave of Data Analytics Solutions.

“Every generational AI problem is actually a data problem to solve, and therefore to shape that data,” said Yasmeen Ahmad (pictured), product manager – data, analytics and AI at Google Cloud. “The challenge has been, as we get closer to generative AI and now multimodal generative AI, images and videos and other modalities, to also provide multimodal data to those generative AI models.”

Ahmad spoke to CUBE Research John FourierExecutive Analyst at theCUBE Research, Supercloud 7: Get Ready for the Next Data Platform event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s live streaming studio. They discussed the challenges and opportunities of multicloud data integration, the criticality of AI-ready data foundations, and the impact of generative AI on data analytics and enterprise data management.

Yasmeen Ahmad speaks with John Furrier about the challenges and opportunities of multi-cloud data integration.

Multicloud integration: connecting data across platforms

Today, businesses often have data scattered across multiple cloud platforms, such as Google, Azure, and Amazon Web Services. Whether through acquisitions, mergers, or the adoption of various SaaS applications, the challenge is to effectively connect and use this distributed data for generative AI applications.

“All large enterprises are multicloud,” Ahmad said. “So the idea of ​​being able to actually connect data and leverage it for AI generation is at the forefront of the minds of the executives I talk to. These AI generation models are hungry for data and training.”

According to Ahmad, the ability to integrate diverse data sources into a cohesive platform is critical to developing advanced AI solutions. When organizations can connect their data, regardless of where it resides, it opens the door to robust data analytics. This democratizes access to data, enabling widespread use of AI technologies across the enterprise. Ahmad sees unified data platforms as key to achieving this integration.

“Enterprises need a single access control plane,” she said. “That’s what we’re really focused on at Google… how can we provide that simplicity of an AI-enabled database that comes with this single control panel, where that database supports all open formats… because today’s data is everywhere and it needs to be interoperable. It needs to be movable.”

Unified Data Platforms: Transforming AI and Analytics

The evolution of traditional data warehouses to modern data platforms such as Google LLC’s Big request represents a significant advancement in the way data is processed and used. These platforms now integrate multiple engines to address different data processing needs, ranging from SQL to Spark and Python, making them more versatile and capable of handling complex AI workloads.

“When we compared BigQuery to other data platforms on the market, we found that performance was actually four times better and cost three times better if the generating AI models were actually close to the data being run,” Ahmad said.

She added that customers don’t face the latency of moving data between data clouds and generating AI models, nor the security concerns, costs and complexities of building pipelines.

According to Ahmad, this unified data platform approach is designed to support a wide range of business intelligence and engineering workloads, enabling organizations to process data in a variety of ways and derive useful insights more efficiently. This shift toward more intelligent and automated data management solutions is poised to revolutionize the way businesses approach data analysis and AI development.

“Today, current-generation AI is an assistant that helps you code faster,” Ahmad said. “But as we move toward this world of agents, we’re seeing agents that can learn to use tools and build pipelines on behalf of the data engineer and really accelerate data analysis to a degree that’s never been seen before.”

According to Ahmad, advances in AI infrastructure and the rise of intelligent data platforms are reshaping the landscape of data analytics and AI development. The integration of multi-cloud environments and unified data platforms such as BigQuery is helping to create more efficient, scalable and intelligent data ecosystems.

“To me, gen AI is a real game changer for data and analytics,” Ahmad said. “I spoke to a customer who… had 16,000 dashboards across the organization, but felt like they were missing insights. Gen AI is going to be a game changer in that space with some of the new conversational analytics experiences we’re launching.”

Stay tuned for the full video interview, part of SiliconANGLE and theCUBE Research’s coverage of the Supercloud 7: Get Ready for the Next Data Platform Event.

Photo: SiliconANGLE