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Google Cloud announced a significant number of new features at its Google Cloud Next event last week, with at least 229 new announcements.
Buried in that mountain of news, which included new AI chips and agentic AI capabilities, as well as database updates, 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.
The stakes are enormous for Google as it takes on rivals in the enterprise data space.
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.
“This is the first year we’ve been given permission to actually post a customer stat, which was delightful for me,” Yasmeen Ahmad, managing director of data analytics at Google Cloud, told VentureBeat. “Databricks and Snowflake, they’re the only other kind of enterprise data warehouse platforms in the market. We have five times more customers than either of them.”
How Google is improving BigQuery to advance enterprise adoption
While Google now claims to have a more extensive user base than its rivals, it’s 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.
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.
This challenge stems from three persistent problems that plague enterprise data management:
Fragmented data silos
Rapidly changing requirements
Inconsistent organizational data cultures where teams don’t share a common language around da …