Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More<\/em><\/p>\n\n\n\n Google Cloud announced a significant number of new features <\/span>at its\u00a0Google Cloud Next\u00a0event last week, with at least 229 new announcements.<\/p>\n\n\n\n Buried in that mountain of news, which included\u00a0new AI chips and agentic AI\u00a0capabilities, as well as\u00a0<\/span>database updat<\/span>es, Google Cloud also made some big moves with its BigQuery data warehouse service. Among the new capabilities is BigQuery Unified Governance, which helps organizations discover, understand and trust their data assets. The governance tools help address key barriers to AI adoption by ensuring data quality, accessibility and trustworthiness.<\/p>\n\n\n\n The stakes are enormous for Google as it takes on rivals in the enterprise data space.<\/p>\n\n\n\n BigQuery has been on the market since 2011 and has grown significantly in recent years, both in terms of capabilities and user base. Apparently, BigQuery is also a big business for Google Cloud. During Google Cloud Next, it was revealed for the first time just how big the business actually is. According to Google, BigQuery had five times the number of customers of both Snowflake and Databricks.<\/p>\n\n\n\n \u201cThis is the first year we\u2019ve been given permission to actually post a customer stat, which was delightful for me,\u201d Yasmeen Ahmad, managing director of data analytics at Google Cloud, told VentureBeat. \u201cDatabricks and Snowflake, they\u2019re the only other kind of enterprise data warehouse platforms in the market. We have five times more customers than either of them.\u201d<\/p>\n\n\n\n While Google now claims to have a more extensive user base than its rivals, it\u2019s not taking its foot off the gas either. In recent months, and particularly at Google Cloud Next, the hyperscaler has announced multiple new capabilities to advance enterprise adoption.<\/p>\n\n\n\n A key challenge for enterprise AI is having access to the correct data that meets business service level agreements (SLAs). According to Gartner research cited by Google, organizations that do not enable and support their AI use cases through an AI-ready data practice will see over 60% of AI projects fail to deliver on business SLAs and be abandoned.<\/p>\n\n\n\n This challenge stems from three persistent problems that plague enterprise data management:<\/p>\n\n\n\n Google\u2019s BigQuery Unified Governance solution represents a significant departure from traditional approaches by embedding governance capabilities directly within the BigQuery platform rather than requiring separate tools or processes.<\/p>\n\n\n\n At the core of Google\u2019s announcement is BigQuery unified governance, powered by the new BigQuery universal catalog. Unlike traditional catalogs that only contain basic table and column information, the universal catalog integrates three distinct types of metadata:<\/p>\n\n\n\n This unified approach allows BigQuery to maintain a comprehensive understanding of data assets across the enterprise. What makes the system particularly powerful is how Google has integrated Gemini, its advanced AI model, directly into the governance layer through what they call the knowledge engine.<\/p>\n\n\n\n The knowledge engine actively enhances governance by discovering relationships between datasets, enriching metadata with business context and monitoring data quality automatically.<\/p>\n\n\n\n Key capabilities include semantic search with natural language understanding, automated metadata generation, AI-powered relationship discovery, data products for packaging related assets, a business glossary, automatic cataloging of both structured and unstructured data and automated anomaly detection.<\/p>\n\n\n\n Google\u2019s strategy transcends the AI model competition.\u00a0<\/p>\n\n\n\n \u201cI think there\u2019s too much of the industry just focused on getting on top of that individual leaderboard, and actually Google is thinking holistically about the problem,\u201d Ahmad said.<\/p>\n\n\n\n This comprehensive approach addresses the entire enterprise data lifecycle, answering critical questions such as: How do you deliver on trust? How do you deliver on scale? How do you deliver on governance and security?<\/p>\n\n\n\n By innovating at each layer of the stack and bringing these innovations together, Google has created what Ahmad calls a real-time data activation flywheel, where, as soon as data is captured, regardless of the type or format or where it\u2019s being stored, there is instant metadata generation, lineage and quality.<\/p>\n\n\n\n That said, models do matter. Ahmad explained that with the advent of thinking models like Gemini 2.0, there has been a huge unlock for Google\u2019s data platforms.<\/p>\n\n\n\n \u201cA year ago, when you were asking GenAI to answer a business question, anything that got slightly more complex, you would actually need to break it down into multiple steps,\u201d she said. \u201cSuddenly, with the thinking model it can come up with a plan\u2026 you\u2019re not having to hard code a way for it to build a plan. It knows how to build plans.\u201d<\/p>\n\n\n\n As a result, she said that now you can easily have a data engineering agent build a pipeline that\u2019s three steps or 10 steps. The integration with Google\u2019s AI capabilities has transformed what\u2019s possible with enterprise data.\u00a0<\/p>\n\n\n\n Levi Strauss & Company offers a compelling example of how unified data governance can transform business operations. The 172-year-old company is using Google\u2019s data governance capabilities as it shifts from being primarily a wholesale business to becoming a direct-to-consumer brand. In a session at Google Cloud Next, Vinay Narayana, who runs data and AI platform engineering at Levi\u2019s, detailed his organization\u2019s use case.<\/p>\n\n\n\n \u201cWe aspire to empower our business analysts to have access to real-time data that is also accurate,\u201d Narayana said. \u201cBefore we embarked on our journey to build a new platform, we discovered various user challenges. Our business users didn\u2019t know where the data lived, and if they knew the data source, they didn\u2019t know who owned it. If they somehow got access, there was no documentation.\u201d<\/p>\n\n\n\n Levi\u2019s built a data platform on Google Cloud that organizes data products by business domain, making them discoverable through Analytics Hub (Google\u2019s data marketplace). Each data product is accompanied by detailed documentation, lineage information and quality metrics.<\/p>\n\n\n\n The results have been impressive: \u201cWe are 50x faster than our legacy data platform, and this is on the low end. A significant number of visualizations are 100x faster,\u201d Narayana said. \u201cWe have over 700 users already using the platform on a daily basis.\u201d<\/p>\n\n\n\n Another example comes from Verizon, which is using Google\u2019s governance tools as part of its One Verizon Data initiative to unify previously siloed data across business units.<\/p>\n\n\n\n \u201cThis is going to be the largest telco data warehouse in North America running on BigQuery,\u201d Arvind Rajagopalan, AVP\u00a0of data engineering, architecture and products at Verizon, said during a Google Cloud Next session.\u00a0<\/p>\n\n\n\n The company\u2019s data estate is massive, comprising 3,500 users who run approximately 50 million queries, 35,000 data pipelines, and over 40 petabytes of data.<\/p>\n\n\n\n In a spotlight session at Google Cloud Next, Ahmad also provided numerous other user examples. Radisson Hotel Group personalized their advertising at scale, training Gemini models on BigQuery data. Teams experienced a 50% increase in productivity, while revenue from AI-powered campaigns rose by more than 20%. Gordon Food Service migrated to BigQuery, ensuring their data was ready for AI and increasing adoption of customer-facing apps by 96%<\/p>\n\n\n\n There are multiple vendors in the enterprise data warehouse space, including Databricks, Snowflake, Microsoft with Synapse and Amazon with Redshift. All of these vendors have been developing various forms of AI integrations in recent years.<\/p>\n\n\n\n Databricks has a comprehensive data bakehouse platform and has been\u00a0expanding<\/span> its own AI capabilities, thanks in part to its $1.3 billion acquisition of Mosaic. Amazon Redshift added support for generative AI in 2023, with Amazon<\/span>\u00a0Q helping users build queries and obtain better answers. For its part, Snowflake has been busy developing tools and\u00a0partnering with large language model (LLM)<\/span> providers,\u00a0including Anthropic.<\/p>\n\n\n\n When pressed on comparisons specifically to Microsoft\u2019s offerings, Ahmad argued that Synapse is not an enterprise data platform for the types of use cases that customers use BigQuery for.<\/p>\n\n\n\n \u201cI think we\u2019ve leapfrogged the entire industry, because we\u2019ve worked on all of the pieces,\u201d she said. \u201cWe\u2019ve got the best model, by the way, it\u2019s the best model integrated in a data stack that understands how agents work.\u201d<\/p>\n\n\n\n This integration has driven rapid adoption of AI capabilities within BigQuery. According to Google, customer use of Google\u2019s AI models in BigQuery for multimodal analysis has increased by 16 times year over year.<\/p>\n\n\n\n For enterprises already struggling with AI implementation, Google\u2019s integrated approach to governance may offer a more streamlined path to success than cobbling together separate data management and AI systems.<\/p>\n\n\n\n Ahmad\u2019s claim that Google has \u201cleapfrogged\u201d competitors in this space will face scrutiny as organizations put these new capabilities to work. However, the customer examples and technical details suggest Google has made significant progress in addressing one of the most challenging aspects of enterprise AI adoption.<\/p>\n\n\n\n For technical decision-makers evaluating data platforms, the key questions will be whether this integrated approach delivers sufficient additional value to justify migrating from existing investments in specialized platforms, such as Snowflake or Databricks, and whether Google can maintain its current innovation pace as competitors respond.<\/p>\n Daily insights on business use cases with VB Daily<\/strong><\/p>\n If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.<\/p>\n \n\t\t\t\t\tThanks for subscribing. Check out more VB newsletters here.\n\t\t\t\t<\/p>\n An error occured.<\/p>\n<\/p><\/div>\n
\n<\/div>How Google is improving BigQuery to advance enterprise adoption<\/h2>\n\n\n\n
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BigQuery unified governance: A technical deep dive<\/h2>\n\n\n\n
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Forget about benchmarks, enterprise AI is a bigger issue<\/h2>\n\n\n\n
Real-world impact: How enterprises are benefiting<\/h2>\n\n\n\n
What\u2019s the \u2018big\u2019 difference: Exploring the competitive landscape<\/h2>\n\n\n\n
What this means for enterprises adopting AI<\/h2>\n\n\n\n
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