Bigquness is the largest 5x of Snowflake and Databrics: What Google is doing to make it better

Google cloud A A large number of new features In that Google Cloud Next It happened last week, with at least 229 new advertisements.

This was buried in Jabal Al -Akhbar, including New artificial intelligence chips and artificial intelligence agent The capabilities, as well as Updat databaseEsGoogle Cloud also made some major moves through the Bigquness Data Warehouse Service. Among the new capabilities is Bigquness University, which helps organizations discover, understand and trust their data assets. Governance tools help in processing the main barriers that prevent the adoption of artificial intelligence by ensuring the quality of data, ease of access and trustworthy.

The risks are huge for Google because it takes its competitors in the institution’s data space.

Bigquness has been working on the market since 2011 and has grown in recent years, both in terms of capabilities and user base. Apparently, Bigquness is also a large Google Cloud company. During Google Cloud Next, it was first unveiled how much business size is actually. According to Google, BigQuery had five times the number of customers of both Snowflake and Databricks.

“This is the first year in which we obtained permission to publish the customer’s statistics already, which is a pleasant matter for me,” Yasmine Ahmed, the administrative director of data analyzes at Google Cloud, told Venturebeat. “Databricks and Snowflake, it is the only type of institution data depot in the market. We have five times more customers than either of them.”

How Google improves Bigquness to enhance the adoption of institutions

Although Google now claims to have a more comprehensive user base than its competitors, it also does not transfer its feet from the gas. In recent months, especially in Google Cloud Next, Hyperscale has announced multiple new capabilities to enhance institutions ’adoption.

One of the main challenges of the AI ​​is to access the correct data that meets the SLAS levels. According to the Gartner Research mentioned by Google, institutions that do not enable and support cases of artificial intelligence by practicing ready -made data for Amnesty International will witness more than 60 % of artificial intelligence projects that fail to provide and abandon SLAS.

This challenge stems from three continuous problems that manages the institution’s data:

1. Feiled data silos
2. Speedly variable requirements
3. Unseen organizational data cultures as the teams do not share a common language about data.

Google’s Google’s Unified Governance Resolution is a significant departure from traditional methods by including governance capabilities directly within the Bigquness platform instead of ordering tools or separate operations.

Uniform Bigquney Governance: Deep Technical Divide

At the heart of the Google advertisement is the unified Bigquness Valley, supported by the new Bigquness Universal catalog. Unlike the traditional catalogs that only contain table information and the basic column, the global catalog integrates three distinctive types of descriptive data:

1. Physical/technical descriptive dataDrivers of the plan, data types and stereotypes.
2. Commercial descriptive dataConditions of the actions of business, descriptions and semantic context.
3. Details of the time of operation: Inquiries, use statistics and information for coordination for technologies such as Apache iceberg.

This unified approach allows Bigquness to maintain a comprehensive understanding of data assets across the organization. What makes the system particularly strong is how Google merged Gemini, advanced artificial intelligence model, directly into the governance layer through what they call the knowledge engine.

The engine is enhanced by the activity of governance by discovering relationships between data groups, enriching descriptive data with the context of work and automatically monitoring data quality.

The main capabilities include semantic research with the understanding of the natural language, generating mechanical descriptive data, discovering the behavior that works on behalf, data products for assets related to packaging, commercial and automatic indexing of both organized and unorganized data and discovering anomalies.

Forget the standards, Enterprise Ai is a bigger problem

The Google strategy exceeds the artificial intelligence model competition.

“I think there is a lot of this industry, which focuses only on obtaining individual leaders, and in fact Google thinks completely about the problem,” Ahmed said.

This comprehensive approach addresses the life cycle of the entire institution’s data, and responding to important questions such as: How can you deliver confidence? How is it widely delivered? How do you receive governance and security?

By innovation in each layer of stack and collecting these innovations together, Google created what Ahmed calls the Data stimulating data in the actual time, where, as soon as the data is taken, regardless of the type, coordination or where it is stored, there is an immediate generation of varieties, quality and quality.

However, models are important. Ahmed explained that with the appearance of thinking models such as Gemini 2.0, there was a large opening for Google data platforms.

“A year ago, when you were asking Genai to answer a commercial question, anything that has become a little more complicated, you will actually need to divide it into multiple steps,” she said. “Suddenly, with the thinking model, it can reach a plan … You do not have to its difficult symbol a way to create a plan. It knows how to build plans.”

As a result, she said now that you can easily get a data engineering agent to create a three -step pipeline or 10 steps. Integration with the abilities of artificial intelligence of Google has transformed what is possible with the institution’s data.

The effect of the real world: How companies benefit

Levi Strauss and Partners It provides a convincing example of how uniform data governance has transferred commercial operations. The 172 -year -old company uses data governance capabilities from Google as it moves from being primarily a wholesale company until it becomes a direct brand of the consumer. In a session at Google Cloud Next, Vinay Naayana, who runs Data and Ai Platform Engineering in Levi’s, detailed the state of his organization’s use.

“We aspire to enable our business analysts to access data in actual time as well.” “Before we start our journey to build a new platform, we discovered the various user challenges. Our business users did not know where the data lived, and if they knew the source of the data, they will not know those who own it. If they were able to reach somehow, there were no documents.”

Levi has built a Google Cloud data platform that regulates data products by business, which makes them discovered through Hub Analytics (Google Data Market). Each data product is accompanied by detailed documents, lineage information and quality standards.

The results were impressive: “We are 50x faster than our old data platform, and this is at the low end. A large number of perceptions are 100x.” “We have more than 700 users who already use the basic system on a daily basis.”

Another example comes from Verizon, which uses Google Governance tools as part of the Verizon One data initiative to unify the data that has already been done through business units.

“This will be the largest warehouse of communications data in North America that works on Bigquness”, Arvind Rajagopalaan, AVP from data engineering, architecture and products in VerizonHe said during the upcoming Google Cloud session.

The company’s data property is huge, and it includes 3,500 users who operate approximately 50 million inquiries, 35,000 data pipelines, and more than 40 bittells of data.

At Google Cloud Next, AHMAD also provided many other user examples. Radisson Hotel Group has allocated its ads widely and training Gemini models on Bigquness data. The teams witnessed a 50 % increase in productivity, while revenues increased from the acting campaigns by more than 20 %. Gordon’s food service has moved to Bigquness, ensuring that their data is ready for males and increased accreditation of applications facing customers by 96 %

What is the “big” difference: exploring the competitive scene

There are many sellers in the area of ​​the Foundation’s data warehouse, including data, snow and microsoft with the clamp and Amazon with red displacement. All of these sellers develop various forms of artificial intelligence in recent years.

Databricks has Lakehouse platform comprehensive data Expansion Its artificial intelligence capabilitiesThanks to part of the mosaic acquisition of $ 1.3 billion. Amazon Redshift added support for artificial intelligence in 2023, with Amazon Q help users to build inquiries and get better answers. For its part, Snowflake was busy developing tools Partnership with the Big Language Model (LLM) Service providers, including humans.

When the comparisons specifically click with Microsoft offers, Ahmed argued that the clamp is not a data platform for the types of cases used by customers.

“I think we jumped the entire industry, because we worked on all the pieces,” she said. “We have the best model, by the way, it’s the best model that is integrated into a staple data that understands how agents work.”

This integration has adopted the possibilities of Amnesty International within Bigquness. According to Google, customers’ use of Amnesty International Models in Google increased in Bigquness for 16 times on a yearly basis on an annual basis.

What does this mean for institutions that adopt artificial intelligence

For institutions that are already struggling with the implementation of artificial intelligence, the integrated Google approach to government may provide a more simplified path of success than collecting separate data and artificial intelligence systems.

Ahmed’s claim that Google has “jump” competitors in this field will face the audit as institutions put these new capabilities to work. However, examples of customer and technical details indicate that Google has made great progress in dealing with one of the most challenging aspects of the Foundation’s AI’s adoption.

For technical decision makers who evaluate data platforms, the main questions are whether this integrated approach provides enough additional value to justify the deportation of current investments in specialized platforms, such as Snowflake or Databrics, and whether Google can maintain the current innovation pace as competitors respond.