Google’s cloud-based approach offers open ecosystem for data sharing

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Today, Google kicked off its Data Cloud Summit with a series of new product announcements and enhancements designed to help data scientists harness the power of Google Cloud Platform for data science. The company has invested heavily in artificial intelligence over the years, and its new products can help businesses and users make sense of the flood of data with traditional analytics and machine learning.

“Data is probably at the top of the agenda for every C-suite on the planet,” said Gerrit Kazmaier, general manager and vice president of databases, data analytics and observer at Google Cloud. “Every company is a big data company. It is multiformat. It’s streaming and it’s everywhere.”

Google wants to compete for that demand with its cloud platform offering sophisticated tools to apply artificial intelligence and machine learning. At the same time, it is fostering an open ecosystem so that companies can use and share data from wherever it is captured. The new releases emphasize breaking down the barriers between different merchants’ clouds and also customers’ own hosting options.

This open strategy can help Google fight against big competitors like Amazon or Microsoft. Amazon Web Services offers nearly a dozen different options for data storage, all of which are tightly integrated with many platforms for data analysis with traditional reporting or machine learning. Microsoft’s Azure also offers a wide range of options that take advantage of its deep history with enterprise computing.

Google’s BigLake platform is designed to work with data across multiple clouds, both stored locally on premise and in commercial clouds, including its competitors. The service can offer companies the opportunity to unify their data lakes and warehouses on a multi-cloud platform.

In the past, many companies created data warehouses, a well-managed model that combined good reporting with strong access control. Lately, some have been using the term “data lake” to describe systems that are more optimized for large tools than sophisticated tools. Google wants to absorb these different approaches with its BigLake model.

“By bringing these worlds together, we take the goodness of one side and apply it to the other side and that way you make your storage infinite,” explained Sudhir Hasbe, director of Google’s Cloud. “You can put as much data as you want. You get the rich governance and management you want in your environment in a hugely changing regulatory environment. You can store all the data and manage and govern it really well.”

cloud alliance

Part of Google’s strategy is to create the Data Cloud Alliance, a collaboration between Google and Confluent, Databricks, Dataiku, Deloitte, Elastic, Fivetran, MongoDB, Neo4j, Redis, and Starburst. The group wants to help standardize open data formats so that information can flow as easily as possible between different clouds across political and corporate barriers.

“We are excited to partner with Google Cloud and members of this Data Cloud Alliance to unify data access across clouds and application environments to remove barriers to digital transformation efforts,” said Mark Porter, CTO of MongoDB. . “Legacy frameworks have made working with data difficult for too many organizations. There couldn’t be a more timely and important data initiative to build faster and smarter data-driven applications for customers.”

At the same time, Google must also watch a growing number of smaller cloud providers, such as Vultr or DigitalOcean, offering prices that are often drastically lower. Google’s deeper commitment to AI research allows them to offer much more sophisticated options than any of these basic cloud service providers.

“The one thing that really sets Google apart is that we believe in developing unique technical products,” said Kazmaier. “Our mindset for innovation is ingrained and understanding data is a vast and limitless resource if harnessed in the right way. The most important thing is that you have to have an open ecosystem around you for it to be successful.”

Vertex AI Workbench is a tool that integrates Jupyter notebooks with major Google cloud components, from data processing instances to serverless and event-driven tools like Spark. The tool can extract information from any of these sources and feed it into analytic routines so data scientists can look for signals in the data. It is provisionally available in some regions on April 6 and everywhere in June.

“At Google Cloud, we are removing the boundaries of data clouds to further reduce the gap between data and AI value.” said June Yang, vice president of cloud AI and innovation at Google. “This capability enables teams to build, train, and deploy models five times faster than traditional laptops.”

The company also wants to encourage teams and companies to share some of the AI ​​models they create. The Vertex AI Model Registry, now in preview, will offer a way for data scientists and application developers to store and reuse AI models.

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