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Today, Google opened their Data Cloud Summit with a slew of announcements of new products and enhancements designed to help data scientists harness the power of the Google Cloud Platform to perform data science. The company has invested heavily in artificial intelligence over the years, and its new products can help businesses and users understand the flow of data with both traditional analytics and machine learning.
“Data is probably at the top of the agenda of any C-suite in the world,” explains Gerrit Kazmaier, general manager and VP for databases, data analytics and viewer at Google Cloud. “Every company is a big data company. It’s multi-format. It’s streaming and it’s everywhere.”
Google wants to respond to that demand with its cloud platform by offering advanced tools for applying artificial intelligence and machine learning. At the same time, it creates an open ecosystem so that companies can use and share data wherever it is captured. The new releases emphasize the breaking of barriers between clouds of different merchants and also self-hosting options by the customers.
This open strategy can help Google compete with big competitors like Amazon or Microsoft. Amazon’s Web Services offers nearly a dozen different data storage options, and all of them are tightly integrated with many data analytics platforms using traditional reports or machine learning. Microsoft’s Azure also offers a wide variety of options that leverage their deep history with enterprise computing.
Google’s BigLake platform is designed to work with data in a variety of clouds, both stored locally on premise and in commercial clouds, including its competitors. The service can offer enterprises the opportunity to unify their data warehouses and lakes in one multi-cloud platform.
In the past, many companies have created data warehouses, a well-governed model that combined good report generation with solid access control. Lately, some have used the term “data lake” to describe systems that are optimized for large rather than sophisticated tools. Google wants to absorb these different approaches with their BigLake model.
“By bringing these worlds together, we take the good from one side and apply it to the other, making your storage infinite,” explains Sudhir Hasbe, director at Google’s Cloud. “You can place as much data as you want. You get the wealth of governance and management you want in your environment in a rapidly changing regulatory environment. You can store, manage and manage all data very well.”
Part of Google’s strategy is to establish the Data Cloud Alliance, a partnership between Google and Confluent, Databricks, Dataiku, Deloitte, Elastic, Fivetran, MongoDB, Neo4j, Redis and Starburst. The group aims to help standardize open formats for data so that information can flow as easily as possible between the different clouds across political and business barriers.
“We are excited to partner with Google Cloud and the members of this Data Cloud Alliance to unify access to data across clouds and application environments to remove barriers to digital transformation efforts,” said Mark Porter, CTO at mongoDB. “Outdated 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 should also keep an eye out for a growing number of smaller cloud vendors like Vultr or DigitalOcean that offer prices that are often significantly lower. Google’s greater commitment to artificial intelligence research allows them to offer much more advanced options than any of these commodity cloud providers.
“The one thing that really sets Google apart is that we believe in developing unique technical products,” says Kazmaier. “Our mindset for innovation is rooted and understanding the data is a huge and limitless resource when used properly. Most importantly, you need to have an open ecosystem around it to be successful.”
The Vertex AI Workbench is a tool that integrates Jupyter notebooks with key components of Google’s Cloud, from data processing instances to serverless to event-driven tools like Spark. The tool can pull information from any of these sources and feed it into analytical routines so data scientists can look for signals in the data. It will be tentatively available in some regions on April 6 and everywhere in June.
“At Google Cloud, we are removing the limits of data clouds to further close the gap between data and AI value.” said June Yang, VP of cloud AI and innovation at Google. “This capability enables teams to build, train, and deploy models five times faster than traditional notebook computers.”
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, provides data scientists and application developers with a way to store and reuse AI models.
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This post Google’s cloud-based approach provides an open ecosystem for data sharing
was original published at “https://venturebeat.com/2022/04/05/googles-cloud-based-approach-offers-open-ecosystem-for-data-sharing/”