Google Cloud on Tuesday revealed plans to inject Gemini into its analytics and data management platforms with the aim of providing customers with a foundation for developing AI models and applications that aid analysis.
Gemini in BigQuery, Gemini in Databases, and Gemini in Looker are now in public preview. All were revealed at Google Cloud Next ’24, the tech giant’s user conference in Las Vegas.
First introduced in December 2023, Gemini is a large language model (LLM) from Google Cloud that allows users to understand and generate text, images, audio, video, code, and other types of information.
When integrated with data management and analysis tools, Gemini and other LLMs such as OpenAI’s ChatGPT and Anthropic’s Claude can help non-technical workers interact with data using the natural language processing (NLP). They can also make data scientists more efficient by reducing time-consuming tasks.
Gemini for Google Cloud includes integrations with the tech giant’s data management and analysis tools. It aims to provide a unified environment in which customers can develop models and applications. using their own proprietary data so that end users can make business-specific decisions with the help of AI.
Unification, meanwhile, is essential, according to Doug Henschen, an analyst at Constellation Research.
Following the launch of ChatGPT in November 2022, many vendors have integrated their products with LLMs to add generative AI (GenAI) capabilities. However, all but some of these integrations were additions. Generative AI capabilities have not been injected into existing tools in a unified way.
It’s not just about adding GenAI features and capabilities. Google’s data and AI strategy is to provide a comprehensive platform with well-integrated features so you can do it all without having to move data around or cobble together disparate services.
Doug HenschenAnalyst, Constellation Research
“It’s not just about adding GenAI features and capabilities,” Henschen said. “Google’s data and AI strategy is to provide a comprehensive platform with well-integrated features so you can do it all without having to move data around or cobble together disparate services.”
Google Cloud is not alone in trying to create a connected environment for AI, he continued. For example, Databricks has capabilities acquired and developed aggressively which allow users to create AI models and applications. Likewise, AWS and Microsoft have developed tools designed to enable AI and analytics in concert.
“The big breakthrough in all hyperscale cloud is offering a single platform for data that seamlessly supports all your AI, GenAI, analytics and application development needs and broader operational frameworks,” Henschen said.
Google Cloud previously introduces integrations between Duet AI, a generative AI platform, and its data management and analysis tools in August 2023. However, the tech giant later integrated Duet AI into Gemini in February. The new merger of Gemini and Google Cloud data management and analysis tools therefore represents an evolution of integrations with Duet AI.
Gemini, data management and analysis
Gemini for BigQuery, Gemini for Looker and Gemini for Databases all aim to help data workers be more productive by allowing them to use AI in concert with their own proprietary data, according to Thomas Kurian, CEO of Google Cloud.
BigQuery is the tech giant’s fully managed data warehouse, Looker is its main analytics platformand Google Cloud offers a series of database options, including AlloyDB and Cloud Spanner.
“Our goal is to provide organizations with a digital platform – powered by AI – to help them accelerate their digital transformation for their business and industry,” Kurian said April 4 during a virtual press conference. “Our data cloud allows users to manage their data, understand insights, and use AI with data to analyze, predict, and summarize insights. »
With Gemini in BigQuery, customers can interact with their data in a notebook-like environment using natural language. Without writing code, users can integrate data and develop pipelines that inform models and applications.
Additionally, Gemini in BigQuery offers users built-in visualizations, AI-augmented data preparation capabilities designed to help clean and discover the most relevant data for an application, query recommendations, and the ability to translate text in SQL or Python code.
A connection between BigQuery and Vertex AI also comes with Gemini in BigQuery. Vertex AI is Google Cloud’s machine learning and generative AI platform that provides access to a multitude of proprietary and open source AI models for offer users choice when deciding which model to use with their data.
Gemini in Looker lets customers essentially chat with their business data, providing an intelligent assistant for self-service and collaborative data analysis.
Conversational Analytics, which is in private preview, is a tool that will allow users to ask questions in natural language. Additionally, features in public preview include functionality that allows users to create natural language visualizations, connect those visualizations with Workspace and also share them with your colleagues without having to write any code.
Finally, Gemini in Databases extends NLP capabilities to the Google Cloud database framework, allowing developers and administrators to build applications and manage data without writing code.
The integration includes SQL generation and summarization in Database Studio, management features in Database Center that allow administrators to monitor all of their databases in a single pane, and intelligent recommendations that provide users with advice on database management.
Additionally, Gemini in BigQuery, Looker and Databases offers features that will benefit Google Cloud data management and analysis users, according to Henschen. The importance of each, however, lies not in their individual components, but in their totality.
“This is significant both in terms of the depth and breadth of GenAI capabilities promised both in each product and across the entire service portfolio,” Henschen said.
For example, Gemini’s presence and support in BigQuery, from data ingestion to preparation, cleansing and pipeline development Before providing query recommendations, he continued.
Similarly, while many vendors add generative AI capabilities to their databases, Google Cloud does so holistically across its database portfolio and with sophisticated features.
“The breadth of Gemini’s support is a differentiator,” Henschen said.
In addition to integrations between Gemini and Google Cloud’s data management and analytics tools, the tech giant unveiled the following features aimed at helping customers derive insights from AI and analytics. data :
Improved support for vector search and storage in BigQuery and AlloyDB to help customers discover relevant data to power retrieval-augmented pipelines that are used to train AI models and applications.
Connections between Vertex AI and BigQuery and AlloyDB to provide access to AI models in Vertex AI.
Natural language search capabilities in Google Distributed Cloud (GDC) built using Gemma, an open model from Google DeepMind, which also allows customers to use third-party models.
Workload performance improvements in GDC powered by GPU and the optimization capabilities of the tensor processing unit.
Integrations with providers like Denodo and MongoDB that expand Google Cloud’s data management and analytics ecosystem.
Just like Gemini for BigQuery, Looker and Databases are designed to power AI development and analysis, so are the many other new features unveiled Tuesday, according to Kurian.
“All of this allows our customers to create their own AI-based applications,” he said.
Seven benefits of generative AI for business.
Next steps
Although generative AI was the priority For many data management and analytics providers for over a year, few tools integrating generative AI with data and analytics are generally available.
There are a few exceptions.
For example, data observability specialist Monte Carlo and data lake specialist Dremio each released generative AI tools in June 2023. Most recently, Tableau launched Pulse, a tool that generates insights and delivers them to users, in February, and MicroStrategy, after initially releasing generally available features in October 2023 , launched an embeddable AI chat tool in March.
Even Google Cloud has made some generative AI features widely available, including AlloyDB AI.
The majority, however, remain at some stage of preview, like those unveiled by Google Cloud on Tuesday. As a result, even if a vendor’s generative AI product development is slightly ahead or behind its competitors, it is far too early for one vendor to be declared significantly more or less innovative than another.
Henschen noted that Google Cloud was slightly behind AWS in adding generative AI to its transactional databases. BigQuery and AI Summitmeanwhile, compare favorably to platforms built by others, as does Google Cloud’s emphasis on openness in terms of allowing customers to use AI models developed by third parties.
Therefore, when it comes to generative AI with data management and analysis, Google Cloud would be wise to continue building on what he has already doneaccording to Henschen.
“I would say Google just needs to stick to its strategy, which has been very consistent,” he said. “Google leads through its strengths. Opening up to third-party vendors and model providers has been another consistent and important part of the Google Cloud strategy.”
Eric Avidon is a senior editor for TechTarget Editorial and a journalist with more than 25 years of experience. It covers data analysis and management.
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