Google Cloud to inject Gemini into data, analytics tools

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Cloud to inject Gemini into data, analytics tools

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Cloud on Tuesday unveiled plans to inject Gemini into its analytics and data management platforms with the goal 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 during

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Cloud Next ’24, the tech giant’s user conference in Las Vegas.

First introduced in February 2024, Gemini is a large language model (LLM) from

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Cloud that enables users to understand and generate text, images, audio, videos, code and other types of information.

When integrated with data management and analytics tools, Gemini and LLMs such as ChatGPT from OpenAI and Claude from Anthropic can help non-technical workers interact with data using natural language processing and make data experts more efficient by reducing time-consuming tasks.

Gemini for

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Cloud includes the integrations with the tech giant’s data management and analytics 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 aided by AI.

Unification, meanwhile, is key, according to Doug Henschen, an analyst at Constellation Research.

After the launch of ChatGPT in November 2022, many vendors integrated with LLMs to add generative AI capabilities. All but some of those integrations were, however, were additions. The generative AI capabilities weren’t injected into existing tools in a unified way.

“It’s not just about adding GenAI features and capabilities,” Henschen said. “

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’s data and AI strategy is about delivering a comprehensive platform with well-integrated capabilities so you can do it all without having to move data around or cobble together disparate services.”

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Cloud is not alone in attempting to create a connected environment for AI, he continued. For example, Databricks has aggressively acquired and developed capabilities that enable users to build AI models and applications.

Similarly, AWS and

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have developed tools designed to foster AI and analytics in concert with one another.

“The big push across all the hyperscale clouds it to offer a single platform for data that seamlessly supports all your AI, GenAI, analytics and wider application development and operational needs,” Henschen said.

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Cloud previously introduced integrations between Duet AI, a generative AI platform, and its data management and analytics tools in August 2023. However, the tech giant subsequently folded Duet AI into Gemini in February 2024. The new fusion of Gemini and

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Cloud’s data management and analytics tools therefore represents an evolution of the integrations with Duet AI.

Gemini, data management and analytics

Gemini for BigQuery, Gemini for Looker and Gemini for Databases all aim to help data workers be more productive by enabling them to use AI in concert with their own proprietary data, according Thomas Kurian,

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Cloud’s CEO.

BigQuery is the tech giant’s fully managed data warehouse,

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and

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Cloud offers a series of database options including AlloyDB and Cloud Spanner.

“Our goal is to provide organizations with a digital platform – AI powered — to help them accelerate their digital transformation for their business and for their industry,” Kurian said on April 4 during a virtual press conference. “Our data cloud allows people to manage their data, understand insights from their data and use AI along with data to analyze, predict and summarize information.”

Using Gemini in BigQuery, customers can engage 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.

In addition, Gemini in BigQuery provides users with embedded visualizations, AI-augmented data preparation capabilities designed to help cleanse and discover the most relevant data for an application, query recommendations and the ability to translate text to SQL or Python code.

A connection between BigQuery and Vertex AI also comes with Gemini in BigQuery. Vertex AI is

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Cloud’s machine learning and generative AI platform and features access to a host of proprietary and open source AI models to

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when deciding which model to use with their data.

Gemini in Looker enables customers to essentially chat with their business data, providing an intelligent assistant for self-service and collaborative data analysis.

Conversation Analytics — which is in private preview — is a tool that will enable users to ask questions using natural language.

In addition, features in public preview include capabilities that enable users to create visualizations using natural language, connect those visualizations with Workspace and share them to co-workers also without having to write code.

Finally, Gemini in Databases extends NLP capabilities to

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Cloud’s cadre of databases, enabling developers and administrators to build applications and govern data without writing code.

The integration includes SQL generation and summarization in Database Studio, management capabilities in Database Center that enable administrators to oversee all their databases in a single pane and smart recommendations that provide users with database management advice.

Separately, Gemini in BigQuery, Looker and databases each provide capabilities that will benefit

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Cloud’s data management and analytics users, according to Henschen. The significance of each, however, ***** not in the individual components but in their comprehensiveness.

“It’s significant both in terms of the depth and breadth of GenAI capabilities promised both within each product and across the entire portfolio of services,” Henschen said.

For example, Gemini’s presence and assistance in BigQuery from data ingestion through preparation, cleansing, and pipeline development before providing query recommendations provides range, he continued.

Similarly, while numerous vendors are adding generative AI capabilities to their databases,

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Cloud is doing it comprehensively across its database portfolio and with sophisticated features.

“The breadth of Gemini assistance is a differentiator,” Henschen said.

In addition to the integrations between Gemini and

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Cloud’s data management and analytics tools, the tech giant unveiled the following capabilities aimed at helping customers derive insights from AI and data analysis:

• Improved support for vector search and storage in BigQuery and AlloyDB to help customers discover relevant data develop to feed the retrieval-augmented generation pipelines that are used to train AI models and applications.
• Connections between Vertex AI and both BigQuery and AlloyDB to provide access to AI models in Vertex AI.
• Natural language search capabilities in
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  Distributed Cloud (GDC) built using Gemma, an open model from
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  DeepMind, that also enable customers to use third-party models.
• Workload performance improvements in GDC fueled by graphics processing unit and tensor processing unit optimization capabilities.

Just as the Gemini for BigQuery, Looker and Databases are designed to foster AI development and analysis, so are the many other new features unveiled on Tuesday, according to Kurian.

“All of this is allowing our customers to build their own AI-powered applications,” he said.

Next steps

Although generative AI has been the primary focus for many data management and analytics vendors for more than a year, few tools integrating generative AI with data and analytics are generally available.

There are some exceptions.

For example, data observability specialist Monte Carlo and data lakehouse specialist Dremio each released generative AI tools in June 2023. More recently, Tableau launched Pulse, a tool that generates insights and delivers them to users, in February 2024 and MicroStrategy, after initially releasing generally available capabilities in October 2023, launched an embeddable AI chat tool in March 2024.

Even

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Cloud has made some generative AI capabilities generally available, including AlloyDB AI.

The majority, however, remain in some stage of preview, such as those unveiled by

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Cloud on Tuesday.

As a result, even if one vendor’s generative AI product development is slightly ahead or behind competitors, it is far too soon for any to be declared significantly more or less innovative than another.

Henschen noted that

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Cloud was slightly behind AWS in adding generative AI to its transactional databases. BigQuery and Vertex AI, meanwhile, compare favorably to platforms being built by others, as does

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Cloud’s emphasis on openness in terms of enabling customers to use AI models developed by third parties.

Therefore, regarding generative AI with data management and analytics,

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Cloud would be wise to continue building on what it has done already, according to Henschen.

“I’d say

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just needs to stick to its strategy, which has been very consistent,” he said. “

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leads with its strengths. Openness to third-party vendors and model providers has been another consistent and important part of the

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Cloud strategy.”

Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with more than 25 years of experience. He covers analytics and data management.

 

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#Cloud #inject #Gemini #data #analytics #tools

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